content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def get_nums(image):
"""get the words from an image using pytesseract.
the extracted words are cleaned and all spaces, newlines and non uppercase
characters are removed.
:param image: inpout image
:type image: cv2 image
:return: extracted words
:rtype: list
"""
# pytesseract c... | 0ff23d8363a14a46c7f6ffa2be130c6eb61409c8 | 3,642,373 |
def create_bag_of_vocabulary_words():
"""
Form the array of words which can be conceived during the game.
This words are stored in hangman/vocabulary.txt
"""
words_array = []
file_object = open("./hangman/vocabulary.txt")
for line in file_object:
for word in line.split():
... | e3aadad2575e28b19b83158eb2127437c8aada89 | 3,642,374 |
import math
def kato_ranking_candidates(identifier: Identifier, params=None):
"""rank candidates based on the method proposed by Kato, S. and Kano, M..
Candidates are the noun phrases in the sentence where the identifier was appeared first.
Args:
identifier (Identifier)
params (dict)
R... | c8a413118b599eb3cb9c9db877d7d489871d65a2 | 3,642,375 |
def _get_bag_of_pos_with_dependency(words, index):
"""Return pos list surrounding index
Args:
words (list): stanfordnlp word list object having pos attributes.
index (int): target index
Return:
pos_list (List[str]): xpos format string list
"""
pos_list = []
def _get_gove... | 02fc508583d79464161927080c1c55d308926274 | 3,642,376 |
def fix_time_individual(df):
"""
1. pandas.apply a jit function to add 0 to time
2. concat date + time
3. change to np.datetime64
"""
@jit
def _fix_time(x):
aux = "0" * (8 - len(str(x))) + str(x)
return aux[:2] + ":" + aux[2:4] + ":" + aux[4:6] + "." + aux[6:]
... | 8d0c99d3f485d852130f9f4fe7ab05bbcdd99557 | 3,642,377 |
def convolve_fft(data, kernel, kernel_fft=False, return_fft=False):
"""
Convolve data with a kernel.
This is inspired by astropy.convolution.convolve_fft, but
stripped down to what's needed for the expected application. That
has the benefit of cutting down on the execution time, but limits
its ... | 64fc4c02f72c419f6c315f524597a32391ea7b8c | 3,642,378 |
def friable_sand(Ks, Gs, phi, phic, P_eff, n=-1, f=1.0):
"""
Friable sand rock physics model.
Reference: Avseth et al., Quantitative Seismic Interpretation, p.54
Inputs:
Ks = Bulk modulus of mineral matrix
Gs = Shear modulus of mineral matrix
phi = porosity
phic = cr... | ace533ee727cd4749ad210b13eec5193b74416b8 | 3,642,380 |
def get_available_currencies():
"""
This function retrieves a listing with all the available currencies with indexed currency crosses in order to
get to know which are the available currencies. The currencies listed in this function, so on, can be used to
search currency crosses and used the retrieved d... | 139f775943bc251149444c702cb4290d78a58a03 | 3,642,381 |
def mktemp(suffix="", prefix=template, dir=None):
"""User-callable function to return a unique temporary file name. The
file is not created.
Arguments are as for mkstemp, except that the 'text' argument is
not accepted.
This function is unsafe and should not be used. The file name
refers to a file that ... | 0785609c3284b0052fa31767d0df11476b28c786 | 3,642,382 |
def getTaskIdentifier( task_id ) :
"""Get tuple of Type and Instance identifiers."""
_inst = Instance.objects.get( id = task_id )
return ( _inst.type.identifier , _inst.identifier ) | fb18be814330bd02205d355b3ebfb68f777ee9c2 | 3,642,383 |
def hessian_vector_product(loss, weights, v):
"""Compute the tensor of the product H.v, where H is the loss Hessian with
respect to the weights. v is a vector (a rank 1 Tensor) of the same size as
the loss gradient. The ordering of elements in v is the same obtained from
flatten_tensor_list() acting on t... | 35ef7772367f56fcded2e4173fe194cb28da3bc7 | 3,642,384 |
def clean_cells(nb_node):
"""Delete any outputs and resets cell count."""
for cell in nb_node['cells']:
if 'code' == cell['cell_type']:
if 'outputs' in cell:
cell['outputs'] = []
if 'execution_count' in cell:
cell['execution_count'] = None
ret... | 67dce7ecc3590143730f943d3eb07ae7df9d8145 | 3,642,385 |
def getProjectProperties():
"""
:return:
@rtype: list of ProjectProperty
"""
return getMetDataLoader().projectProperties | 7f517a20d83002c41867bbc7911f775d64b21b88 | 3,642,387 |
def svn_client_cleanup(*args):
"""svn_client_cleanup(char dir, svn_client_ctx_t ctx, apr_pool_t scratch_pool) -> svn_error_t"""
return _client.svn_client_cleanup(*args) | 2a9921e8521e927e124633bb932b158a1f9abdf3 | 3,642,388 |
def model_chromatic(psrs, psd='powerlaw', noisedict=None, components=30,
gamma_common=None, upper_limit=False, bayesephem=False,
wideband=False,
idx=4, chromatic_psd='powerlaw', c_psrs=['J1713+0747']):
"""
Reads in list of enterprise Pulsar instance an... | 568f4951930fe6f8175417785c4503895f76bc88 | 3,642,389 |
def test_f32(heavydb):
"""If UDF name ends with an underscore, expect strange behaviour. For
instance, defining
@heavydb('f32(f32)', 'f32(f64)')
def f32_(x): return x+4.5
the query `select f32_(0.0E0))` fails but not when defining
@heavydb('f32(f64)', 'f32(f32)')
def f32_(x): retu... | 157560cc90e3f869d84198eeb26896a76157eb39 | 3,642,391 |
from typing import Union
from pathlib import Path
def get_message_bytes(
file_path: Union[str, Path],
count: int,
) -> bytes:
"""
从 GRIB2 文件中读取第 count 个要素场，裁剪区域 (东北区域)，并返回新场的字节码
Parameters
----------
file_path
count
要素场序号，从 1 开始，ecCodes GRIB Key count
Returns
... | 6a2ad3a20e02283c2bffe31eb78cacf84d92ff6f | 3,642,392 |
from typing import Union
from typing import List
import json
def discover_climate_observations(
time_resolution: Union[
None, str, TimeResolution, List[Union[str, TimeResolution]]
] = None,
parameter: Union[None, str, Parameter, List[Union[str, Parameter]]] = None,
period_type: Union[None, str... | b96fd2a0a9bcb9a7b50018a1b7e3ae7add3e3c63 | 3,642,393 |
def set_template(template_name, file_name, p_name):
"""
Insert template into the E-mail.
"""
corp = template(template_name, file_name, p_name)
msg = MIMEMultipart()
msg['from'] = p_name
msg['subject'] = f'{file_name}'
msg.attach(MIMEText(corp, 'html'))
return msg | 8745d9729ddbe159e0bca90dee198ce4e3efb489 | 3,642,394 |
import gettext
def lazy_gettext(string):
"""A lazy version of `gettext`."""
if isinstance(string, _TranslationProxy):
return string
return _TranslationProxy(gettext, string) | 9229c987d6b2f300f7225ea4b58f964c70e882fc | 3,642,395 |
def toggleautowithdrawalstatus(status, fid, alternate_token=False):
"""
Sets auto-withdrawal status of the account associated
with the current OAuth token under the specified
funding ID.
:param status: Boolean for toggle.
:param fid: String with funding ID for target account
:return: String ... | 4df2be7801a23978c58b7ce8aec7e5fd30fb1e76 | 3,642,396 |
def load_avenger_models():
"""
Load each instance of data from the repository into its associated model at this point in the schema lifecycle
"""
avengers = []
for item in fetch_avenger_data():
# Explicitly assign each attribute of the model, so various attributes can be ignored
ave... | 70740495be63a198cf5ec1308608955f52be46f0 | 3,642,397 |
import json
import _json
import _datetime
def aggregate_points(point_layer,
bin_type=None,
bin_size=None,
bin_size_unit=None,
polygon_layer=None,
time_step_interval=None,
time_step_interval_un... | fe946d4273ed1ce4e4cd3e46d9f9a3e0ff5c6725 | 3,642,398 |
def scattered_embedding_lookup(params,
values,
dimension,
name=None,
hash_key=None):
"""Looks up embeddings using parameter hashing for each value in `values`.
The i-th embedding component of... | a317d7d494bd9b9918f6f2354d854c2fbffc1c6c | 3,642,399 |
from typing import Iterable
from typing import Callable
def get_features_and_labels(instances: Iterable[NewsHeadlineInstance],
feature_generator: Callable[[NewsHeadlineInstance],
dict[str]]) -> tuple[list[dict[str]], list[int]]:
"... | 56d2f1a0a18eb1d1f8ecf9547184ae873d0b60e3 | 3,642,401 |
def countBarcodeStats(bcseqs,chopseqs='none',bcs = ["0","1"],use_specific_beginner=None):
"""this function uses edlib to count the number of matches to given bcseqs.
chopseqs can be left, right, both, or none. This tells the program to
chop off one barcode from either the left, right, both, or non... | af19f5a77f241362d50245885ab15dabd5197dcd | 3,642,402 |
def is_underflow(bin_nd, hist):
"""Retuns whether global bin number bin_nd is an underflow bin. Works
for any number of dimensions
"""
flat1d_bin = get_flat1d_bin(bin_nd, hist, False)
return flat1d_bin == 0 | 377c5a339f404ef4e55832f163952575f7b8d6a4 | 3,642,403 |
def deprecated_func_docstring(foo=None):
"""DEPRECATED. Deprecated function."""
return foo | f9c996c4f3735ed2767f0bbb139b1494e2a0fa39 | 3,642,404 |
def get_all_nodes(starting_node : 'NodeDHT') -> 'list[NodeDHT]':
"""Return all nodes in the DHT"""
nodes = [starting_node]
node = starting_node
while node != starting_node:
node = node.succ
nodes.append(node)
return nodes | 91b2968b000abac3d6f9f51bad5889ccf0fe8388 | 3,642,405 |
import re
def by_regex(regex_tuples, default=True):
"""Only call function if
regex_tuples is a list of (regex, filter?) where if the regex matches the
requested URI, then the flow is applied or not based on if filter? is True
or False.
For example:
from aspen.flows.filter import by_rege... | a3d47690120a8091596047d73792b0d1f637132b | 3,642,407 |
def deserialize(name):
"""Get the activation from name.
:param name: name of the method.
among the implemented Keras activation function.
:return:
"""
name = name.lower()
if name == SOFTMAX:
return backward_softmax
if name == ELU:
return backward_elu
if name == SEL... | 133f01edaa678d60f85bf720590c0df3d1c552f3 | 3,642,408 |
def delete_item_image(itemid, imageid):
"""
Delete an image from item.
Args:
itemid (int) - item's id
imageid (int) - image's id
Status Codes:
204 No Content – when image deleted successfully
"""
path = '/items/{}/images/{}'.format(itemid, imageid)
return delete(pa... | 28d3c7bea85cd7132de6010def1c2ec41a9cfc82 | 3,642,409 |
def bytes_(s, encoding='utf-8', errors='strict'): # pragma: no cover
"""Utility to ensure binary-like usability.
If ``s`` is an instance of ``text_type``, return
``s.encode(encoding, errors)``, otherwise return ``s``"""
if isinstance(s, text_type):
return s.encode(encoding, errors)
return... | 269d315c1204be941766558fc3cbbc07c8e63657 | 3,642,410 |
from operator import inv
import numpy
def normal_transform(matrix):
"""Compute the 3x3 matrix which transforms normals given an affine vector transform."""
return inv(numpy.transpose(matrix[:3,:3])) | b7f7256b9057b9a77b074080e698ff859ccbefb2 | 3,642,412 |
async def async_unload_entry(hass, config_entry):
"""Unload OMV config entry."""
unload_ok = await hass.config_entries.async_unload_platforms(
config_entry, PLATFORMS
)
if unload_ok:
controller = hass.data[DOMAIN][config_entry.entry_id]
await controller.async_reset()
hass... | 60955e2aac51d211a296de0736f784c2332f855b | 3,642,413 |
import typing
import csv
def create_prediction_data(validation_file: typing.IO) -> dict:
"""Create a dictionary object suitable for prediction."""
validation_data = csv.DictReader(validation_file)
races = {}
# Read each horse from each race
for row in validation_data:
race_id = row["Entry... | 6ec67b277460feb5d80bf7a35e7bc40f3014e6ce | 3,642,414 |
def username(request):
""" Returns ESA FTP username """
return request.config.getoption("--username") | 2393884c2c9f65055cd7a14c1b732fccf70a6e28 | 3,642,415 |
def complete_data(df):
"""Add some temporal columns to the dataset
- day of the week
- hour of the day
- minute
Parameters
----------
df : pandas.DataFrame
Input data ; must contain a `ts` column
Returns
-------
pandas.DataFrame
Data with additional columns `da... | be342df461c04fc4b7f5b757f8287973c8826bd8 | 3,642,416 |
import re
def is_valid_mac_address_normalized(mac):
"""Validates that the given MAC address has
what we call a normalized format.
We've accepted the HEX only format (lowercase, no separators) to be generic.
"""
return re.compile('^([a-f0-9]){12}$').match(mac) is not None | 7c4ea0a3353a3753907de21bbf114b2a228bb3c0 | 3,642,417 |
def get_Y(data):
"""
Function: convert pandas data table to sklearn Y variable
Arguments
---------
data: panadas data table
Result
------
Y[:,:]: float
sklearn Y variable
"""
return np.array((data["H"],data["sigma"])).T | d5e9d5b116fe8e82165d019c23394b6f1dfc4d9c | 3,642,418 |
def get_bbox(mask, show=False):
"""
Get the bbox for a binary mask
Args:
mask: a binary mask
Returns:
bbox: (col_min, col_max, row_min, row_max)
"""
area_obj = np.where(mask != 0)
bbox = np.min(area_obj[0]), np.max(area_obj[0]), np.min(area_obj[1]), np.max(area_obj[1])
i... | 2e074d305d50334809eb0fe3e15def6fd4d21644 | 3,642,419 |
from pineboolib.core import settings
def check_mobile_mode() -> bool:
"""
Return if you are working in mobile mode, searching local settings or check QtCore.QSysInfo().productType().
@return True or False.
"""
return (
True
if QtCore.QSysInfo().productType() in ("android", "ios")... | 99327efbc3d329218d027e4451aae1979a9ebccc | 3,642,420 |
def check_for_overflow_candidate(node):
"""
Checks if the node contains an expression which can potentially produce an overflow
meaning an expression which is not wrapped by any cast, which involves the operator
+, ++, *, **. Note, the expression can have several sub-expression. It is the case
of the expression (a... | 77232f5d94a6cba6fef79bd51886145e2dfec4bf | 3,642,421 |
import struct
def parse_monitor_message(msg):
"""decode zmq_monitor event messages.
Parameters
----------
msg : list(bytes)
zmq multipart message that has arrived on a monitor PAIR socket.
First frame is::
16 bit event id
32 bit event value
no pad... | df71541d34bc04b1ac25c6435b1b298394e27362 | 3,642,422 |
import toml
import json
def load_config(fpath):
"""
Load configuration from fpath and return as AttrDict.
:param fpath: configuration file path, either TOML or JSON file
:return: configuration object
"""
if fpath.endswith(".toml"):
data = toml.load(fpath)
elif fpath.endswith(".jso... | 27c68c944a431b4d8b12c6b64609f33043363b03 | 3,642,423 |
def softmax_layer(inputs, n_hidden, random_base, drop_rate, l2_reg, n_class, scope_name='1'):
"""
Method adapted from Trusca et al. (2020). Encodes the sentence representation into a three dimensional vector
(sentiment classification) using a softmax function.
:param inputs:
:param n_hidden:
:p... | 1f77d99d12c927c0d77e136098fe8f9c2bc458b8 | 3,642,424 |
def node2freqt(docgraph, node_id, child_str='', include_pos=False,
escape_func=FREQT_ESCAPE_FUNC):
"""convert a docgraph node into a FREQT string."""
node_attrs = docgraph.node[node_id]
if istoken(docgraph, node_id):
token_str = escape_func(node_attrs[docgraph.ns+':token'])
if... | 8c6690e5fec41f98501060f5bf24ed823a2c31b6 | 3,642,425 |
def search(news_name):
"""method to fetch search results"""
news_name_list = news_name.split(" ")
search_name_format = "+".join(news_name_list)
searched_results = search_news(search_name_format)
sourcess=get_source_news()
title = f'search results for {news_name}'
return render_template('sea... | 7521221b66a872b00310693a3ccc6c81013098a2 | 3,642,429 |
def encrypt_document(document):
"""
Useful method to encrypt a document using a random cipher
"""
cipher = generate_random_cipher()
return decrypt_document(document, cipher) | 9a7e4bd79a83df261c4f946f62ff9bf40bfbf068 | 3,642,430 |
def bootstrap_alert(visitor, items):
"""
Format:
[[alert(class=error)]]:
message
"""
txt = []
for x in items:
cls = x['kwargs'].get('class', '')
if cls:
cls = 'alert-%s' % cls
txt.append('<div class="alert %s">' % cls)
if 'clos... | c2803176b2e1ed9b3d4aecd622eedcac673d4c42 | 3,642,431 |
def masked_mean(x, *, mask, axis,
paxis_name, keepdims):
"""Calculates the mean of a tensor, excluding masked-out entries.
Args:
x: Tensor to take the mean of.
mask: Boolean array of same shape as 'x'. True elements are included in the
mean, false elements are excluded.
axis: Axis... | 3242e86f571af61909efa63bd60158aa0f8eba88 | 3,642,432 |
def aspectRatioFix(preserve,anchor,x,y,width,height,imWidth,imHeight):
"""This function helps position an image within a box.
It first normalizes for two cases:
- if the width is None, it assumes imWidth
- ditto for height
- if width or height is negative, it adjusts x or y and makes them positive
... | 73a686f122ad31ee6693641e1ef386f13b67b4d8 | 3,642,433 |
import random
def circle_area(radius: int) -> float:
""" estimate the area of a circle using the monte carlo method.
Note that the decimal precision is log(n). So if you want a precision of
three decimal points, n should be $$ 10 ^ 3 $$.
:param r (int): the radius of the circle
:return (int): the ... | 2c85759ffbf798749263fca368cdfd159d67028b | 3,642,435 |
def Quantized_MLP(pre_model, args):
"""
quantize the MLP model
:param pre_model:
:param args:
:return:
"""
#full-precision first and last layer
weights = [p for n, p in pre_model.named_parameters() if 'fp_layer' in n and 'weight' in n]
biases = [pre_model.fp_layer2.bias]
#layer... | cd5b36c1b10567fee5a8b1f10679e6868f42f98f | 3,642,436 |
def _super_tofrom_choi(q_oper):
"""
We exploit that the basis transformation between Choi and supermatrix
representations squares to the identity, so that if we munge Qobj.type,
we can use the same function.
Since this function doesn't respect :attr:`Qobj.type`, we mark it as
private; only thos... | da91aff35d891000773100b998b80dc5d998414f | 3,642,437 |
def get_attention_weights(data):
"""Get the attention weights of the given function."""
# USE INTERACTIONS
token_interaction = data['tokeninteraction']
df_token_interaction = pd.DataFrame(token_interaction)
# check clicked tokens to draw squares around them
clicked_tokens = np.array(data['final... | e3189bd67f3da6ee8c1173348eec249d9c8cfa9a | 3,642,438 |
def save_ecg_example(gen_data: np.array, image_name, image_title='12-lead ECG'):
"""
Save 12-lead ecg signal in fancy .png
:param gen_data:
:param image_name:
:param image_title:
:return:
"""
fig = plt.figure(figsize=(12, 14))
for _lead_n in range(gen_data.shape[1]):
curr_lea... | 456fa204b20eee53645a900614877a6fb6a53e9c | 3,642,439 |
async def async_unload_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
"""Unload an entry."""
component: EntityComponent = hass.data[DOMAIN]
return await component.async_unload_entry(entry) | b4ae648493b63a27f5127139876cf0bca2a2dcbb | 3,642,440 |
def run_random_climate(gdir, nyears=1000, y0=None, halfsize=15,
bias=None, seed=None, temperature_bias=None,
climate_filename='climate_monthly',
climate_input_filesuffix='', output_filesuffix='',
init_area_m2=None, unique_sample... | 2887c1e62d3357e028c7be0539225bfb879323d9 | 3,642,442 |
from typing import Optional
def sync_get_ami_arch_from_instance_type(instance_type: str, region_name: Optional[str]=None) -> str:
"""For a given EC2 instance type, returns the AMI architecture associated with the instance type
Args:
instance_type (str): An EC2 instance type; e.g., "t2.micro"
region_n... | 2289deea91c9a9dafa0492fac9230292b546e9b7 | 3,642,443 |
import math
def atan2(y, x):
"""Returns angle of a 2D coordinate in the XY plane"""
return math.atan2(y, x) | ede5a647c175bebf2800c22d92e396deff6077e2 | 3,642,444 |
def index_objects(
*, ids, indexer_class, index=None, transforms=None, manager_name=None
):
"""
Index specified `ids` in ES using `indexer_class`. This is done in a single
bulk action.
Pass `index` to index on the specific index instead of the default index
alias from the `indexed_class`.
... | c93ea99946bb1516a58bb39aa5d43b1644f4f4da | 3,642,445 |
def get_attrs_titles_with_transl() -> dict:
"""Returns attribut titles and translation"""
attr_titles = []
attrs = Attribute.objects.filter(show_in_list=True).order_by('weight')
for attr in attrs:
attr_titles.append(attr.name)
result = {}
for title in attr_titles:
result[title] ... | 167955e669ddb3f6d5bbbd48cc01d26155a9e4ba | 3,642,446 |
def kde_KL_divergence_2d(x, y, h_x, h_y, nb_bins=100, fft=True):
"""Uses Kernel Density Estimator with Gaussian kernel on two
dimensional samples x and y and returns estimated Kullback-
Leibler divergence.
@param x, y: samples, given as a (n, 2) shaped numpy array,
@param h: width of the Gaussian k... | ce7ef19846dfd729fe5703aceaec69392f455ca6 | 3,642,447 |
def gml_init(code):
"""
Initializes a Group Membership List (GML) for schemes of the given type.
Parameters:
code: The code of the scheme.
Returns:
A native object representing the GML. Throws an Exception on error.
"""
gml = lib.gml_init(code)
if gml == ffi.NULL:
... | 5558f2db6a1c2269796cd52f675d5579ce357949 | 3,642,448 |
def before_run(func, force=False):
"""
Adds a function *func* to the list of callbacks that are invoked right before luigi starts
running scheduled tasks. Unless *force* is *True*, a function that is already registered is not
added again and *False* is returned. Otherwise, *True* is returned.
"""
... | 378604f6c574345682d8bd3d155ef8e4344aac27 | 3,642,449 |
def calc_z_scores(baseline, seizure):
""" This function is meant to generate the figures shown in the Brainstorm
demo used to select the 120-200 Hz frequency band. It should also
be similar to panel 2 in figure 1 in David et al 2011.
This function will compute a z-score for each value of the seizure p... | db3f6fbc42450658700ca2d120bf6faa31fccdfd | 3,642,450 |
def get_column(data, column_index):
"""
Gets a column of data from the given data.
:param data: The data from the CSV file.
:param column_index: The column to copy.
:return: The column of data (as a list).
"""
return [row[column_index] for row in data] | 3fd5c8c76ccfed145aba0e685aa57ad01b3695a5 | 3,642,451 |
def analytic_solution(num_dims,
t_val,
x_val=None,
domain_bounds=(0.0, 1.0),
x_0=(0.5, 0.5),
d=1.0,
k_decay=0.0,
k_influx=0.0,
trunc_order=100,
... | 0a920ec22fbe1ae3ff510ddd4389c1cf4ae0912d | 3,642,452 |
def safe_gas_limit(*estimates: int) -> int:
"""Calculates a safe gas limit for a number of gas estimates
including a security margin
"""
assert None not in estimates, "if estimateGas returned None it should not reach here"
calculated_limit = max(estimates)
return int(calculated_limit * constants... | 439eca363dc1fe1f53972c69191513913feef39b | 3,642,453 |
import typing
def integer_years(dates: typing.Any) -> typing.List[int]:
"""Maps a list of 'normalized_date' strings to a sorted list of integer years.
Args:
dates: A list of strings containing dates in the 'normalized_date' format.
Returns:
A list of years extracted from "dates".
""... | cdf14f0a2fee197177f12ead43346dfd4eabb5ef | 3,642,454 |
def add_wmts_gibs_basemap(ax, date='2016-02-05'):
"""http://gibs.earthdata.nasa.gov/"""
URL = 'http://gibs.earthdata.nasa.gov/wmts/epsg4326/best/wmts.cgi'
wmts = WebMapTileService(URL)
# Layers for MODIS true color and snow RGB
# NOTE: what other tiles available?: TONS!
#https://wiki.earthdata.... | 434ff85e1a721937ba83d0438bb7384d1a1f0600 | 3,642,455 |
import torch
def encode_position(
batch_size: int,
axis: list,
max_frequency: float,
num_frequency_bands: int,
sine_only: bool = False,
) -> torch.Tensor:
"""
Encode the Fourier Features and return them
Args:
batch_size: Batch size
axis: List containing the size of eac... | 06a81219b85006226069b288cce8602fc62e7119 | 3,642,456 |
def expr_erode(src, size = 5):
"""
Same result as core.morpho.Erode(), faster and workable in 32 bit.
"""
expr = _morpho_matrix(size, mm = 'min')
return core.akarin.Expr(src, expr) | 06f76f889cadcec538639ca1a920168c6a9ec467 | 3,642,457 |
def response_modification(response):
"""
Modify API response format.
"""
if (
status.is_client_error(response.status_code)
or status.is_server_error(response.status_code)
) and (status.HTTP_400_BAD_REQUEST != response.status_code):
return response
# Modify the response d... | f8a3120f3a1671d71f32158b742212b896074bdc | 3,642,458 |
import trace
def process_source_lineage(grid_sdf, data_sdf, value_field=None):
"""
performs the operation to generate the
"""
try:
subtypes = arcpy.da.ListSubtypes(data_sdf)
st_dict = {}
for stcode, stdict in list(subtypes.items()):
st_dict[stcode] = subtypes[stcod... | 298e615474debbb01addc583ae19fc1c5191084b | 3,642,460 |
def class_to_mask(classes: np.ndarray, class_colors: np.ndarray) -> np.ndarray:
"""クラスIDの配列をRGBのマスク画像に変換する。
Args:
classes: クラスIDの配列。 shape=(H, W)
class_colors: 色の配列。shape=(num_classes, 3)
Returns:
ndarray shape=(H, W, 3)
"""
return np.asarray(class_colors)[classes] | c574594b18d312e9ce432b68c8c2ff4d73771e6f | 3,642,461 |
from typing import List
import logging
def get_vocab(iob2_files:List[str]) -> List[str]:
"""Retrieve the vocabulary of the iob2 annotated files
Arguments:
iob2_files {List[str]} -- List of paths to the iob2 annotated files
Returns:
List[str] -- Returns the unique list of vocabula... | 0dc2a1f969ed6f92b36b1b31875c855d5efda2d9 | 3,642,462 |
import numpy
def taylor_green_vortex(x, y, t, nu):
"""Return the solution of the Taylor-Green vortex at given time.
Parameters
----------
x : numpy.ndarray
Gridline locations in the x direction as a 1D array of floats.
y : numpy.ndarray
Gridline locations in the y direction as a 1... | f47f4cdf11b81fe8b8c38ae50d708ec4361f7098 | 3,642,463 |
def static_initial_state(batch_size, h_size):
""" Function to make an initial state for a single GRU.
"""
state = jnp.zeros([h_size], dtype=jnp.complex64)
if batch_size is not None:
state = add_batch(state, batch_size)
return state | a803da5b0af0ce17fc7d1f303f6141416da6d120 | 3,642,464 |
def get_desklamp(request, index):
"""
A pytest fixture to initialize and return the DeskLamp object with
the given index.
"""
desklamp = DeskLamp(index)
try:
desklamp.open()
except RuntimeError:
pytest.skip("Could not open desklamp connection")
def fin():
desklamp... | 8f00296f5625c8a80bb094d1e470936a0733b83e | 3,642,465 |
import torch
def conj(x):
"""
Calculate the complex conjugate of x
x is two-channels complex torch tensor
"""
assert x.shape[-1] == 2
return torch.stack((x[..., 0], -x[..., 1]), dim=-1) | b22cfd3f12759f9b237099ca0527f0cbe9b99348 | 3,642,466 |
def label_clusters(img, min_cluster_size=50, min_thresh=1e-6, max_thresh=1, fully_connected=False):
"""
Label Clusters
"""
dim = img.dimension
clust = threshold_image(img, min_thresh, max_thresh)
temp = int(fully_connected)
args = [dim, clust, clust, min_cluster_size, temp]
processed_arg... | efe63ea0e71d3a5bf3b2f0a03f3c0f1c295c063b | 3,642,467 |
def update_schema(schema_old, schema_new):
"""
Given an old BigQuery schema, update it with a new one.
Where a field name is the same, the new will replace the old. Any
new fields not present in the old schema will be added.
Arguments:
schema_old: the old schema to update
schema_ne... | e97827ac0d8ee943b88fc54506af3f6fc8285d71 | 3,642,468 |
def get_estimators(positions_all, positions_relevant):
"""
Extracts density estimators from a judged sample of paragraph positions.
Parameters
----------
positions_all : dict of (Path, float)
A sample of paragraph positions from various datasets in the NTCIR-11
Math-2, and NTCIR-12 ... | b5f95247ff683e6e7e86d425ec64c988daacab60 | 3,642,469 |
def openbabel_force_field(label, mol, num_confs=None, xyz=None, force_field='GAFF', return_xyz_strings=True,
method='diverse'):
"""
Optimize conformers using a force field (GAFF, MMFF94s, MMFF94, UFF, Ghemical)
Args:
label (str): The species' label.
mol (Molecule, ... | 9964d94d2601e5cd7871886e396778457bb6e2cd | 3,642,470 |
def parse_flarelabels(label_file):
"""
Parses a flare-label file and generates a dictionary mapping residue identifiers (e.g. A:ARG:123) to a
user-specified label, trees that can be parsed by flareplots, and a color indicator for vertices.
Parameters
----------
label_file : file
A flare... | 23df49af14af720311b320f65894e995983365bf | 3,642,471 |
def remove_background(data, dim="t2", deg=0, regions=None):
"""Remove polynomial background from data
Args:
data (DNPData): Data object
dim (str): Dimension to perform background fit
deg (int): Polynomial degree
regions (None, list): Background regions, by default entire region ... | 54141b6f28b7a21ebdf1b0b920af3bfea4303b07 | 3,642,472 |
def get_hmm_datatype(query_file):
"""Takes an HMM file (HMMer3 software package) and determines what data
type it has (i.e., generated from an amino acid or nucleic acid alignment).
Returns either "prot" or "nucl".
"""
datatype = None
with open(query_file) as infh:
for i in infh:
... | 27653784b8a9fbae92226f8ea7d7b6e2b647765e | 3,642,473 |
def detect_min_threshold_outliers(series, threshold):
"""Detects the values that are lower than the threshold passed
series : series, mandatory
The series where to detect the outliers
threshold : integer, float, mandatory
The threshold of the minimum value that will be consid... | 6032693341073d101c0aad598a105f6cbc0ec578 | 3,642,474 |
from datetime import datetime
def new_datetime(d):
"""
Generate a safe datetime from a datetime.date or datetime.datetime object.
"""
kw = [d.year, d.month, d.day]
if isinstance(d, real_datetime):
kw.extend([d.hour, d.minute, d.second, d.microsecond, d.tzinfo])
return datetime(*kw) | 58479d70918dd287bfd29b1a15b6cd4dc1bfd695 | 3,642,475 |
def _to_str(x):
"""Converts a bool tensor to a string with True/False values."""
x = tf.convert_to_tensor(x)
if x.dtype == tf.bool:
return tf.where(x, 'True', 'False')
return x | 7919139e0f2cb19cd0856110e962acb616193ada | 3,642,476 |
def inpaintn(x,m=100, x0=None, alpha=2):
""" This function interpolates the input (2-dimensional) image 'x' with missing values (can be NaN of Inf). It is based on a recursive process
where at each step the discrete cosine transform (dct) is performed of the residue, multiplied by some weights, and then th... | 2fddabc6e512f9fc1ae7e8298f8d44582eaf7c46 | 3,642,477 |
def obtain_bboxs(path) -> list:
"""
obatin bbox annotations from the file
"""
file = open(path, "r")
lines = file.read().split("\n")
lines = [x for x in lines if x and not x.startswith("%")]
lines = [x.rstrip().lstrip() for x in lines] # get rid of fringe whitespaces
bboxs = []
for... | 75ceaac4bd8500320007d2ffb4cf4c490bd29473 | 3,642,478 |
def Timeline_Integral_with_cross_before(Tm,):
"""
计算时域金叉/死叉信号的累积卷积和(死叉(1-->0)不清零，金叉(0-->1)清零)
这个我一直不会写成 lambda 或者 apply 的形式，只能用 for循环，谁有兴趣可以指导一下
"""
T = [Tm[0]]
for i in range(1,len(Tm)):
T.append(T[i - 1] + 1) if (Tm[i] != 1) else T.append(0)
return np.array(T) | fdbd68e84e2a79a96c2078f92a7b69ab0138874e | 3,642,479 |
from typing import Generator
def list_image_paths() -> Generator[str, None, None]:
"""List each image path in the input directory."""
return list_input_directory(INPUT_DIRECTORIES["image_dir"]) | bce70f2af3c42905a27a30bf97de0a993161130f | 3,642,480 |
def a_star(graph: Graph, start: Node, goal: Node, heuristic):
"""
Standard A* search algorithm.
:param graph: Graph A graph with all nodes and connections
:param start: Node Start node, where the search starts
:param goal: Node End node, the goal for the search
:return: shortest_path: list|False Either a ... | ca25a15733d041cfca2560164ea8b047e55991b8 | 3,642,481 |
def buildAndTrainModel(model, learningRate, batchSize, epochs, trainingData, validationData, testingData, trainingLabels, validationLabels, testingLabels, MODEL_NAME, isPrintModel=True):
"""Take the model and model parameters, build and train the model"""
# Build and compile model
# To use other optimi... | af00f383311588525e66cff317908a99fa39859f | 3,642,482 |
def gaussian_temporal_filter(tsincr: np.ndarray, cutoff: float, span: np.ndarray,
thr: int) -> np.ndarray:
"""
Function to apply a Gaussian temporal low-pass filter to a 1D time-series
vector for one pixel with irregular temporal sampling.
:param tsincr: 1D time-series vecto... | 54060dbfc84ce1738698fda893afb556b48396e4 | 3,642,483 |
import requests
import json
def get_mactable(auth):
"""
Function to get list of mac-addresses from Aruba OS switch
:param auth: AOSSAuth class object returned by pyarubaoss.auth
:return list of mac-addresses
:rtype list
"""
url_mactable = "http://" + auth.ipaddr + "/rest/" + auth.version ... | 8f81a03640d7a4ed0d6d70bcaf268b647dee987e | 3,642,484 |
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