content stringlengths 22 815k | id int64 0 4.91M |
|---|---|
def parse(data):
"""
Parses the input of the Santander text file.
The format of the bank statement is as follows:
"From: <date> to <date>"
"Account: <number>"
"Date: <date>"
"Description: <description>"
"Amount: <amount>"
"Balance: <amount>"
<second_tra... | 5,330,200 |
def solve_duffing(tmax, dt_per_period, t_trans, x0, v0, gamma, delta, omega):
"""Solve the Duffing equation for parameters gamma, delta, omega.
Find the numerical solution to the Duffing equation using a suitable
time grid: tmax is the maximum time (s) to integrate to; t_trans is
the initial time perio... | 5,330,201 |
def create_prog_assignment_registry():
"""Create the registry for course properties."""
reg = FieldRegistry(
'Prog Assignment Entity', description='Prog Assignment',
extra_schema_dict_values={
'className': 'inputEx-Group new-form-layout'})
# Course level settings.
course_op... | 5,330,202 |
def _read_one_cml(cml_g,
cml_id_list=None,
t_start=None,
t_stop=None,
column_names_to_read=None,
read_all_data=False):
"""
Parameters
----------
cml_g
cml_id_list
t_start
t_stop
column_names_to_rea... | 5,330,203 |
def get_commands():
"""
returns a dictionary with all the az cli commands, keyed by the path to the command
inside each dictionary entry is another dictionary of verbs for that command
with the command object (from cli core module) being stored in that
"""
# using Microsoft VSCode tooling ... | 5,330,204 |
def test_getNum():
"""Test fcn that returns selected nums[i]"""
new_num = 321
i = 2
u = Blockchain().address(0) # btw, this account is no longer owner
c = Contract('test')
c.connect()
c.run_trx(u, 'storeNum', i, new_num)
assert c.call_fcn('getNum', i) == new_num | 5,330,205 |
def calculate_keypoints(img, method, single_channel, graphics=False):
"""
Gray or single channel input
https://pysource.com/2018/03/21/feature-detection-sift-surf-obr-opencv-3-4-with-python-3-tutorial-25/
"""
if single_channel=='gray':
img_single_channel = single_channel_gray(img)
... | 5,330,206 |
def plot_pos_neg(
train_data: pd.DataFrame,
train_target: pd.DataFrame,
col1: str = 'v5',
col2: str = 'v6'
) -> None:
"""
Make hexbin plot for training transaction data
:param train_data: pd.DataFrame, features dataframe
:param train_target: pd.DataFrame, target dataframe... | 5,330,207 |
def open_w_lock (file_name, mode = "r", bufsize = -1) :
"""Context manager that opens `file_name` after successfully locking it.
"""
with lock_file (file_name) :
with open (file_name, mode, bufsize) as file :
yield file | 5,330,208 |
def raffle_form(request, prize_id):
"""Supply the raffle form."""
_ = request
prize = get_object_or_404(RafflePrize, pk=prize_id)
challenge = challenge_mgr.get_challenge()
try:
template = NoticeTemplate.objects.get(notice_type='raffle-winner-receipt')
except NoticeTemplate.DoesNotExist:... | 5,330,209 |
def is_rldh_label(label):
"""Tests a binary string against the definition of R-LDH label
As defined by RFC5890_
Reserved LDH labels, known as "tagged domain names" in some
other contexts, have the property that they contain "--" in the
third and fourth characters but which otherwise co... | 5,330,210 |
def offence_memory_patterns(obs, player_x, player_y):
""" group of memory patterns for environments in which player's team has the ball """
def environment_fits(obs, player_x, player_y):
""" environment fits constraints """
# player have the ball
if obs["ball_owned_player"] == obs["activ... | 5,330,211 |
def get_mem() -> int:
"""Return memory used by CombSpecSearcher - note this is actually the
memory usage of the process that the instance of CombSpecSearcher was
invoked."""
return int(psutil.Process(os.getpid()).memory_info().rss) | 5,330,212 |
def load_parameters(directory_name):
"""
Loads the .yml file parameters to a dictionary.
"""
root = os.getcwd()
directory = os.path.join(root, directory_name)
parameter_file_name = directory
parameter_file = open(parameter_file_name, 'r')
parameters = yaml.load(parameter_file, Loader=yam... | 5,330,213 |
def aggregated_lineplot_new(df_agg,countries,fill_between=('min','max'),save=False,fig=None,ax=None,clrs='default'):
"""
Creates an aggregates lineplot for multiple countries
Arguments:
*df_agg* (DataFrame) : contains the aggregated results, either relative (df_rel) or absolute (df_abs)
*co... | 5,330,214 |
def _stat_categories():
"""
Returns a `collections.OrderedDict` of all statistical categories
available for play-by-play data.
"""
cats = OrderedDict()
for row in nfldb.category.categories:
cat_type = Enums.category_scope[row[2]]
cats[row[3]] = Category(row[3], row[0], cat_type, ... | 5,330,215 |
def rsafactor(d: int, e: int, N: int) -> List[int]:
"""
This function returns the factors of N, where p*q=N
Return: [p, q]
We call N the RSA modulus, e the encryption exponent, and d the decryption exponent.
The pair (N, e) is the public key. As its name suggests, it is public and is used to
... | 5,330,216 |
def integrate_prob_current(psi, n0, n1, h):
"""
Numerically integrate the probability current, which is
Im{psi d/dx psi^*} over the given spatial interval.
"""
psi_diff = get_imag_grad(psi, h)
curr = get_prob_current(psi, psi_diff)
res = np.zeros(psi.shape[0])
with progressbar.Prog... | 5,330,217 |
def getFile(path):
"""
指定一个文件的路径，放回该文件的信息。
:param path: 文件路径
:return: PHP-> base64 code
"""
code = """
@ini_set("display_errors","0");
@set_time_limit(0);
@set_magic_quotes_runtime(0);
$path = '%s';
$hanlder = fopen($path, 'rb');
$res = fread($hanlder, filesize($path));
fc... | 5,330,218 |
def _load():
"""Load the previous state of the repository."""
if not os.path.exists(CONFIG_PATH):
print("[x] No config file available.")
return
with open(CONFIG_PATH) as file_handle:
try:
CONFIG.update(json.load(file_handle))
except ValueError:
print(... | 5,330,219 |
def rgc(tmpdir):
""" Provide an RGC instance; avoid disk read/write and stay in memory. """
return RGC(entries={CFG_GENOMES_KEY: dict(CONF_DATA),
CFG_FOLDER_KEY: tmpdir.strpath,
CFG_SERVER_KEY: "http://staging.refgenomes.databio.org/"}) | 5,330,220 |
def test_riid_generator_length():
"""The RIID generator length should be a non-zero length string."""
assert isinstance(config.riid_generator_length, int)
assert 0 < config.riid_generator_length | 5,330,221 |
def execlog(command): # logs commands and control errors
"""
controling the command executions using os.system, and logging the commands
if an error raise when trying to execute a command, stops the script and writting the
rest of commands to the log file after a 'Skipping from here' note.
"""
global skipping
t... | 5,330,222 |
def simulate_quantities_of_interest_superoperator(tlist, c_ops, noise_parameters_CZ, fluxlutman,
fluxbias_q1, amp,
sim_step,
verbose: bool=True):
"""
Calculates the propagator and the quantities of intere... | 5,330,223 |
def setup_logging(loglevel):
"""Setup basic logging
Args:
loglevel (int): minimum loglevel for emitting messages
"""
logformat = '[%(asctime)s] %(levelname)s:%(name)s: %(message)s'
logging.basicConfig(level=loglevel, stream=sys.stdout,
format=logformat, datefmt='%Y-%m... | 5,330,224 |
def test_query_with_parquet(sdc_builder, sdc_executor, cluster, database):
"""Validate end-to-end case with stopping the pipeline and executing the map/reduce job after it read all the
data from database. Addresses Hive drift synchronization solution in parquet data format. The pipeline looks like:
jdb... | 5,330,225 |
def __polyline():
"""Read polyline in from package data.
:return:
"""
polyline_filename = resource_filename(
'cad', join(join('data', 'dxf'), 'polyline.dxf'))
with open(polyline_filename, 'r') as polyline_file:
return polyline_file.read() | 5,330,226 |
def create_identity_split(all_chain_sequences, cutoff, split_size,
min_fam_in_split):
"""
Create a split while retaining diversity specified by min_fam_in_split.
Returns split and removes any pdbs in this split from the remaining dataset
"""
dataset_size = len(all_chain_seq... | 5,330,227 |
def get_SNR(raw, fmin=1, fmax=55, seconds=3, freq=[8, 13]):
"""Compute power spectrum and calculate 1/f-corrected SNR in one band.
Parameters
----------
raw : instance of Raw
Raw instance containing traces for which to compute SNR
fmin : float
minimum frequency that is used for fitt... | 5,330,228 |
def get_all_child_wmes(self):
""" Returns a list of (attr, val) tuples representing all wmes rooted at this identifier
val will either be an Identifier or a string, depending on its type """
wmes = []
for index in range(self.GetNumberChildren()):
wme = self.GetChild(index)
if wme.IsI... | 5,330,229 |
def scan_image_directory(path):
"""Scan directory of FITS files to create basic stats.
Creates CSV file ready to be read by pandas and print-out of the stats if
less than 100 entries.
Parameters
----------
path : str, pathlib.Path
Returns
-------
pd.DataFrame
DataFrame con... | 5,330,230 |
def print_configure_help_info():
"""
print configuration tips
"""
sys.stdout.write('Please run the command first: duedge configure ')
sys.stdout.write('--access-key=<your access-key> ')
sys.stdout.write('--secret-key=<your secret-key> ')
sys.stdout.write('to initialize local user configurati... | 5,330,231 |
def test_function3():
"""tests the key guessing function"""
basepath = os.path.join(os.getcwd(), "MusicXML_files")
filenames = os.listdir(basepath)
for filename in filter(lambda s: s.endswith(".mxl"), filenames):
m = MusicXMLExtractor(os.path.join(basepath, filename))
m.read_xml_... | 5,330,232 |
def load_mat(filename):
"""
Reads a OpenCV Mat from the given filename
"""
return read_mat(open(filename, 'rb')) | 5,330,233 |
def data_consist_notebook(table1, table2, key1, key2, schema1, schema2, fname, output_root=''):
"""
Automatically generate ipynb for checking data consistency
Parameters
----------
table1: pandas DataFrame
one of the two tables to compare
table2: pandas DataFrame
one of the two ... | 5,330,234 |
def preprocess_fmri(rawdata=None):
"""example of a preprocessing function
Args:
rawdata (pyrsa.data.dataset.Dataset): the neural data
Returns:
preprocessed neural data in format of measurements,
descriptors, obs_descriptors, channel_descriptors
Example:... | 5,330,235 |
def connected_components(image, threshold, min_area, max_area, max_features, invert=False):
"""
Detect features using connected-component labeling.
Arguments:
image (float array): The image data. \n
threshold (float): The threshold value. \n
...
Returns:
features (pa... | 5,330,236 |
def reduce_scan(row, params, **kwargs):
"""
Reduce scan-mode grism data
.. warning::
This function is not yet implemented. It will raise an exception.
Parameters
----------
row : abscal.common.exposure_data_table.AbscalDataTable
Single-row table of the exposure to be ex... | 5,330,237 |
def module(spec):
""" Returns the module at :spec:
@see Issue #2
:param spec: to load.
:type spec: str
"""
cwd = os.getcwd()
if cwd not in sys.path:
sys.path.append(cwd)
return importlib.import_module(spec) | 5,330,238 |
def transform_digits_to_string(labels: Tuple[str], coefficients,
offset: Fraction) -> str:
"""Form a string from digits.
Arguments
---------
labels: the tuple of lablels (ex.: ('x', 'y', 'z') or ('a', 'b', 'c')))
coefficients: the parameters in front of label ... | 5,330,239 |
def match_peaks_with_mz_info_in_spectra(spec_a, spec_b, ms2_ppm=None, ms2_da=None):
"""
Match two spectra, find common peaks. If both ms2_ppm and ms2_da is defined, ms2_da will be used.
:return: list. Each element in the list is a list contain three elements:
m/z from spec 1; i... | 5,330,240 |
def make_recsim_env(
recsim_user_model_creator: Callable[[EnvContext], AbstractUserModel],
recsim_document_sampler_creator: Callable[[EnvContext], AbstractDocumentSampler],
reward_aggregator: Callable[[List[AbstractResponse]], float],
) -> Type[gym.Env]:
"""Creates a RLlib-ready gym.Env class given RecS... | 5,330,241 |
def test_dpp_auth_resp_retries(dev, apdev):
"""DPP Authentication Response retries"""
check_dpp_capab(dev[0])
check_dpp_capab(dev[1])
dev[0].set("dpp_resp_max_tries", "3")
dev[0].set("dpp_resp_retry_time", "100")
logger.info("dev0 displays QR Code")
addr = dev[0].own_addr().replace(':', '')... | 5,330,242 |
def set_default_subparser(self, name, args=None):
"""
see http://stackoverflow.com/questions/5176691/argparse-how-to-specify-a-default-subcommand
"""
subparser_found = False
for arg in sys.argv[1:]:
if arg in ['-h', '--help']: # global help if no subparser
break
else:
... | 5,330,243 |
def unflatten(dictionary, delim='.'):
"""Breadth first turn flattened dictionary into a nested one.
Arguments
---------
dictionary : dict
The dictionary to traverse and linearize.
delim : str, default='.'
The delimiter used to indicate nested keys.
"""
out = defaultdict(di... | 5,330,244 |
def read_array(dtype, data):
"""Reads a formatted string and outputs an array.
The format is as for standard python arrays, which is
[array[0], array[1], ... , array[n]]. Note the use of comma separators, and
the use of square brackets.
Args:
data: The string to be read in.
dtype: The data... | 5,330,245 |
def in_ipynb():
"""
Taken from Adam Ginsburg's SO answer here:
http://stackoverflow.com/a/24937408/4118756
"""
try:
cfg = get_ipython().config
if cfg['IPKernelApp']['parent_appname'] == 'ipython-notebook':
return True
else:
return False
except Name... | 5,330,246 |
def load_bioschemas_jsonld_from_html(url, config):
"""
Load Bioschemas JSON-LD from a webpage.
:param url:
:param config:
:return: array of extracted jsonld
"""
try:
extractor = bioschemas.extractors.ExtractorFromHtml(config)
filt = bioschemas.filters.BioschemasFilter(confi... | 5,330,247 |
def pformat(dictionary, function):
"""Recursively print dictionaries and lists with %.3f precision."""
if isinstance(dictionary, dict):
return type(dictionary)((key, pformat(value, function)) for key, value in dictionary.items())
# Warning: bytes and str are two kinds of collections.Container, but w... | 5,330,248 |
def _tonal_unmodulo(x):
"""
>>> _tonal_unmodulo((0,10,0))
(0, -2, 0)
>>> _tonal_unmodulo((6,0,0))
(6, 12, 0)
>>> _tonal_unmodulo((2, 0))
(2, 0)
"""
d = x[0]
c = x[1]
base_c = MS[d].c
# Example: Cb --- base=0 c=11 c-base=11 11 - 12 = -1
if c - base_c > 6:
... | 5,330,249 |
def get_ncopy(path, aboutlink = False):
"""Returns an ncopy attribute value (it is a requested count of
replicas). It calls gfs_getxattr_cached."""
(n, cc) = getxattr(path, GFARM_EA_NCOPY, aboutlink)
if (n != None):
return (int(n), cc)
else:
return (None, cc) | 5,330,250 |
def cache_bottlenecks(sess, image_lists, image_dir, bottleneck_dir,
jpeg_data_tensor, bottleneck_tensor):
"""Ensures all the training, testing, and validation bottlenecks are cached.
Because we're likely to read the same image multiple times (if there are no
distortions applied during train... | 5,330,251 |
def has_three_or_more_vowels(string):
"""Check if string has three or more vowels."""
return sum(string.count(vowel) for vowel in 'aeiou') >= 3 | 5,330,252 |
def write_to_xlsx(
fund_infos: list[FundInfo],
xlsx_filename: Path,
logger: Logger = Logger.null_logger(),
) -> None:
"""
Structuralize a list of fund infos to an Excel document.
Input: a list of fund infos, and an Excel filename.
"""
# TODO profile to see whether and how much setting ... | 5,330,253 |
def lbfgs_inverse_hessian_factors(S, Z, alpha):
"""
Calculates factors for inverse hessian factored representation.
It implements algorithm of figure 7 in:
Pathfinder: Parallel quasi-newton variational inference, Lu Zhang et al., arXiv:2108.03782
"""
J = S.shape[1]
StZ = S.T @ Z
R = jnp... | 5,330,254 |
def get_geojson_observations(properties: List[str] = None, **kwargs) -> Dict[str, Any]:
""" Get all observation results combined into a GeoJSON ``FeatureCollection``.
By default this includes some basic observation properties as GeoJSON ``Feature`` properties.
The ``properties`` argument can be used to over... | 5,330,255 |
def createFilter(fc, Q, fs):
"""
Returns digital BPF with given specs
:param fc: BPF center frequency (Hz)
:param Q: BPF Q (Hz/Hz)
:param fs: sampling rate (Samp/sec)
:returns: digital implementation of BPF
"""
wc = 2*pi*fc
num = [wc/Q, 0]
den = [1, wc/Q, wc**2]
dig_tf = sign... | 5,330,256 |
def delete_tc_policy_class(device, parent, classid, namespace=None):
"""Delete a TC policy class of a device.
:param device: (string) device name
:param parent: (string) qdisc parent class ('root', 'ingress', '2:10')
:param classid: (string) major:minor handler identifier ('10:20')
:param namespace... | 5,330,257 |
def create_uno_struct(cTypeName: str):
"""Create a UNO struct and return it.
Similar to the function of the same name in OOo Basic.
Returns:
object: uno struct
"""
oCoreReflection = get_core_reflection()
# Get the IDL class for the type name
oXIdlClass = oCoreReflection.forNam... | 5,330,258 |
def get_dir():
"""Return the location of resources for report"""
return pkg_resources.resource_filename('naarad.resources',None) | 5,330,259 |
def p_atl_suite(p) :
"""
atl_suite : NEWLINE INDENT atl_line_stmts DEDENT
"""
p[0] = p[3] | 5,330,260 |
async def multiwalk(ip, community, oids,
port=161, timeout=6, fetcher=multigetnext):
# type: (str, str, List[str], int, int, Callable[[str, str, List[str], int, int], List[VarBind]]) -> Generator[VarBind, None, None]
"""
Executes a sequence of SNMP GETNEXT requests and returns an async_g... | 5,330,261 |
def checkout_commit(repo, commit_id):
"""
Context manager that checks out a commit in the repo.
"""
current_head = repo.head.commit if repo.head.is_detached else repo.head.ref
try:
repo.git.checkout(commit_id)
yield
finally:
repo.git.checkout(current_head) | 5,330,262 |
def SoftCrossEntropyLoss(input, target):
"""
Calculate the CrossEntropyLoss with soft targets
:param input: prediction logicts
:param target: target probabilities
"""
total_loss = torch.tensor(0.0)
for i in range(input.size(1)):
cls_idx = torch.full((input.size(0),), i, dtype=torch.... | 5,330,263 |
def test_base_mult_list_is_empty_without_base_lists():
"""
test baseMultList.is_empty is True when there are no baseLists
"""
assert base_mult_list.is_empty() is True | 5,330,264 |
def composite_layer(inputs, mask, hparams):
"""Composite layer."""
x = inputs
# Applies ravanbakhsh on top of each other.
if hparams.composite_layer_type == "ravanbakhsh":
for layer in xrange(hparams.layers_per_layer):
with tf.variable_scope(".%d" % layer):
x = common_layers.ravanbakhsh_set_l... | 5,330,265 |
def edit_expense(expense_id, budget_id, date_incurred, description, amount, payee_id):
"""
Changes the details of the given expense.
"""
query = sqlalchemy.text("""
UPDATE budget_expenses
SET
budget_id = (:budget_id),
date_incurred = (:date_incurred),
description = (:description),
... | 5,330,266 |
def get_mnist_iterator(batch_size, input_shape, num_parts=1, part_index=0):
"""Returns training and validation iterators for MNIST dataset
"""
get_mnist_ubyte()
flat = False if len(input_shape) == 3 else True
train_dataiter = mx.io.MNISTIter(
image="data/train-images-idx3-ubyte",
l... | 5,330,267 |
def dataset():
"""Get data frame for test purposes."""
return pd.DataFrame(
data=[['alice', 26], ['bob', 34], ['claire', 19]],
index=[0, 2, 1],
columns=['Name', 'Age']
) | 5,330,268 |
def main(number_sites_along_xyz=10, steps=25000, external_field_sweep_start=1,
external_field_sweep_end=11, temperature_sweep_start=1,
temperature_sweep_end=11):
"""Run simulation over a sweep of temperature and external field values.
Parameters
----------
number_sites_along_xyz : int... | 5,330,269 |
def get_consumer_key():
"""This is entirely questionable. See settings.py"""
consumer_key = None
try:
loc = "%s/consumer_key.txt" % settings.TWITTER_CONSUMER_URL
url = urllib2.urlopen(loc)
consumer_key = url.read().rstrip()
except (urllib... | 5,330,270 |
def write_validation3_set_to_file(file, outfile):
"""
单独写标签3.0体系到文件
"""
tag3 = ["社会", "体育", "娱乐", "财经", "时政", "科技", "时尚", "教育", "情感", "文化",
"旅游", "美食", "宠物", "星座命理", "搞笑", "壁纸头像", "生活", "职场", "小说",
"国际", "房产", "汽车", "军事", "游戏", "动漫", "育儿", "健康", "历史", "儿童",
"知识", "其他... | 5,330,271 |
def disconnect() -> Tuple[str, int]:
"""Deletes the DroneServerThread with a given id.
Iterates over all the drones in the shared list and deletes the one with a
matching drone_id. If none are found returns an error.
Request:
drone_id (str): UUID of the drone.
Response:
Tuple[str,... | 5,330,272 |
def add_company(context: Context, company: Company):
"""Will add an Company to Scenario Data.
:param context: behave `context` object
:param company: an instance of Company Tuple
"""
assert isinstance(
company, Company
), "Expected Company named tuple but got '{}' instead".format(type(c... | 5,330,273 |
def get_graph_feature(x, k=20, idx=None, x_coord=None):
"""
Args:
x: (B, d, N)
"""
batch_size = x.size(0)
num_points = x.size(2)
x = x.view(batch_size, -1, num_points)
if idx is None:
if x_coord is None: # dynamic knn graph
idx = knn(x, k=k)
else: ... | 5,330,274 |
def get_markers(
image_array: np.ndarray,
evened_selem_size: int = 4,
markers_contrast_times: float = 15,
markers_sd: float = 0.25,
) -> np.ndarray:
"""Finds the highest and lowest grey scale values for image flooding."""
selem = smo.disk(evened_selem_size)
evened = sfi.rank.mean_bilateral(
... | 5,330,275 |
def pd_bigdata_read_csv(file, **pd_read_csv_params):
"""
读取速度提升不明显
但是内存占用显著下降
"""
reader = pd.read_csv(file, **pd_read_csv_params, iterator=True)
loop = True
try:
chunk_size = pd_read_csv_params['chunksize']
except:
chunk_size = 1000000
chunks = []
while loop:
... | 5,330,276 |
def PCA(Y_name, input_dim):
"""
Principal component analysis: maximum likelihood solution by SVD
Adapted from GPy.util.linalg
Arguments
---------
:param Y: NxD np.array of data
:param input_dim: int, dimension of projection
Returns
-------
:rval X: - Nxinput_dim np.array of dimensionality reduced data
W - i... | 5,330,277 |
def data_cache_path(page, page_id_field='slug'):
"""
Get (and make) local data cache path for data
:param page:
:return:
"""
path = os.path.join(CACHE_ROOT, '.cache', 'data', *os.path.split(getattr(page, page_id_field)))
if not os.path.exists(path):
sh.mkdir('-p', path)
return pa... | 5,330,278 |
def _get_sequence(value, n, channel_index, name):
"""Formats a value input for gen_nn_ops."""
# Performance is fast-pathed for common cases:
# `None`, `list`, `tuple` and `int`.
if value is None:
return [1] * (n + 2)
# Always convert `value` to a `list`.
if isinstance(value, list):
pass
elif isin... | 5,330,279 |
def make_linear(input_dim, output_dim, bias=True, std=0.02):
"""
Parameters
----------
input_dim: int
output_dim: int
bias: bool
std: float
Returns
-------
torch.nn.modules.linear.Linear
"""
linear = nn.Linear(input_dim, output_dim, bias)
init.normal_(linear.weight, ... | 5,330,280 |
def matnorm_logp_conditional_col(x, row_cov, col_cov, cond, cond_cov):
"""
Log likelihood for centered conditional matrix-variate normal density.
Consider the following partitioned matrix-normal density:
.. math::
\\begin{bmatrix}
\\operatorname{vec}\\left[\\mathbf{X}_{i j}\\right] \\\... | 5,330,281 |
def get_map_folderpath(detectionID):
"""
Make sure map directory exists and return folder location for maps to be
saved to.
"""
homedir = os.path.dirname(os.path.abspath(__file__))
if not os.path.exists('map'):
os.makedirs('map')
detection_folder = 'map/'+str(detection... | 5,330,282 |
def get_next_by_date(name, regexp):
"""Get the next page by page publishing date"""
p = Page.get(Page.name == name)
query = (Page.select(Page.name, Page.title)
.where(Page.pubtime > p.pubtime)
.order_by(Page.pubtime.asc())
.dicts())
for p in ifilter(lambda x: regexp.m... | 5,330,283 |
def relative_performance(r_df, combinations, optimal_combinations, ref_method='indp', ref_jt='nan', ref_at='nan',
ref_vt='nan', cost_type='Total', deaggregate=False):
"""
This functions computes the relative performance, relative cost, and univeral
relative measure :cite:`Talebiyan2... | 5,330,284 |
def generate_raw_mantissa_extraction(optree):
""" generate an operation graph to extraction the significand field
of floating-point node <optree> (may be scalar or vector).
The implicit bit is not injected in this raw version """
if optree.precision.is_vector_format():
base_precision = o... | 5,330,285 |
def read_temp_f(p):
"""
read_temp_f
Returns the temperature from the probe in degrees farenheit
p = 1-Wire device file
"""
lines = read_temp_raw(p)
while lines[0].strip()[-3:] != 'YES':
time.sleep(0.2)
lines = read_temp_raw(p)
equals_pos = lines[1].find('t=')
if ... | 5,330,286 |
def count_parameters(model, trainable_only=True, is_dict=False):
"""
Count number of parameters in a model or state dictionary
:param model:
:param trainable_only:
:param is_dict:
:return:
"""
if is_dict:
return sum(np.prod(list(model[k].size())) for k in model)
if ... | 5,330,287 |
def send_mail(text, to_addr, account):
""" Send the email using msmtp. account is the account in .msmtprc
"""
# check_call does not take input in Python 3.4.
# But check_output does??
dummy = subprocess.check_output(
['msmtp', '-d', '-a', account, to_addr],
input=text.encode()
) | 5,330,288 |
def setup_conf(conf=cfg.CONF):
"""Setup the cfg for the status check utility.
Use separate setup_conf for the utility because there are many options
from the main config that do not apply during checks.
"""
common_config.register_common_config_options()
neutron_conf_base.register_core_common_co... | 5,330,289 |
def release_kind():
"""
Determine which release to make based on the files in the
changelog.
"""
# use min here as 'major' < 'minor' < 'patch'
return min(
'major' if 'breaking' in file.name else
'minor' if 'change' in file.name else
'patch'
for file in pathlib.Pat... | 5,330,290 |
def assert_array_equal(x: List[list], y: List[list]):
"""
usage.scipy: 1
"""
... | 5,330,291 |
def edit_text_file(filepath: str, regex_search_string: str, replace_string: str):
"""
This function is used to replace text inside a file.
:param filepath: the path where the file is located.
:param regex_search_string: string used in the regular expression to find what has to be replaced.
:param re... | 5,330,292 |
def test_parent_dataset_links(some_interdeps):
"""
Test that we can set links and retrieve them when loading the dataset
"""
links = generate_some_links(3)
ds = DataSet()
for link in links:
link.head = ds.guid
ds.set_interdependencies(some_interdeps[1])
ds.parent_dataset_link... | 5,330,293 |
def find_sums(sheet):
"""
Tallies the total assets and total liabilities for each person.
RETURNS:
Tuple of assets and liabilities.
"""
pos = 0
neg = 0
for row in sheet:
if row[-1] > 0:
pos += row[-1]
else:
neg += row[-1]
return pos, neg | 5,330,294 |
def read_links(title):
"""
Reads the links from a file in directory link_data.
Assumes the file exists, as well as the directory link_data
Args:
title: (Str) The title of the current wiki file to read
Returns a list of all the links in the wiki article with the name title
"""
with... | 5,330,295 |
def test_compile_model_from_params():
"""Tests that if build_fn returns an un-compiled model,
the __init__ parameters will be used to compile it
and that if build_fn returns a compiled model
it is not re-compiled.
"""
# Load data
data = load_boston()
X, y = data.data[:100], data.target[:... | 5,330,296 |
def writeFEvalsMaxSymbols(fevals, maxsymbols, isscientific=False):
"""Return the smallest string representation of a number.
This method is only concerned with the maximum number of significant
digits.
Two alternatives:
1) modified scientific notation (without the trailing + and zero in
th... | 5,330,297 |
def closedcone(r=1, h=5, bp=[0,0,0], sampH=360, sampV=50, fcirc=20):
"""
Returns parametrization of a closed cone with radius 'r' and height 'h at
basepoint (bpx,bpy,bpz), where 'sampH' and 'sampV' specify the amount of
samples used horizontally, i.e. for circles, and vertically, i.e.
for height,... | 5,330,298 |
def E_lndetW_Wishart(nu,V):
"""
mean of log determinant of precision matrix over Wishart <lndet(W)>
input
nu [float] : dof parameter of Wichart distribution
V [ndarray, shape (D x D)] : base matrix of Wishart distribution
"""
if nu < len(V) + 1:
raise ValueError, "dof parameter n... | 5,330,299 |
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