content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def build_table_schema(data, index=True, primary_key=None, version=True):
"""
Create a Table schema from ``data``.
Parameters
----------
data : Series, DataFrame
index : bool, default True
Whether to include ``data.index`` in the schema.
primary_key : bool or None, default True
... | bb84be571d2e151285099a9a1f2c44b93427cced | 3,601,387 |
def remove_non_ascii(s):
"""
Remove non-ascii characters in a file. Needed when support for non-ASCII
is not available.
Args:
s (str): Input string
Returns:
String with all non-ascii characters removed.
"""
return "".join(i for i in s if ord(i) < 128) | 0a215ffa1841667d7dd7d9c1d9a12bbe84e2cbcd | 3,601,389 |
def gaussian_z(covar):
"""Single-dimensional Gaussian PDF normalizing coefficient.
Args:
covar (scalar): Covariance of the Gaussian PDF
Returns:
float: Normalizing coefficient Z
"""
return np.sqrt(2.0 * np.pi * covar) | 3691a37f5925cf2c12c54e8a31649ad09ece0297 | 3,601,390 |
def _hoeffding_interval(sample: np.ndarray, ref_size, c, delta, mode) -> float:
"""
A helper function calculates the concentration interval using Hoeffding inequality.
No assumptions over the sampled distribution.
Parameters
----------
sample : np.ndarray
Sample data
ref_size : (int... | a51e72a3ac6fd14ce6469a670e5982727add3d3e | 3,601,391 |
def animation_make(year_range, save_name = None, approx=10):
"""
Makes an animation for the given diagram. Optionally, it saves
Parameters
----------
year_range : TYPE
DESCRIPTION.
save_name : TYPE, optional
DESCRIPTION. The default is None.
approx : TYPE, optional
S... | 67076212eed0c989b0f2b8bd824f2548dbadbbc7 | 3,601,392 |
def get_acis_fp_limits(states, acis_s=-111.0, acis_i=-112.0):
""" Get ACIS Focal Plane limits based on SIM position.
This function uses SIM position to assign temperature limits.
Args:
states (ndarray): States array, in the form returned by the cmd_states.get_cmd_states.fetch_states function
... | ae7aca016d8af77dae3c8c079c12ca3577cc81e9 | 3,601,393 |
from functools import reduce
def compose(fns, x=nil):
"""Takes a set of functions and returns a fn that is the composition
of those fns. The returned fn takes a variable number of args,
applies the rightmost of fns to the args, the next
fn (left-to-right) to the result, etc. If no value is supplied, returns a
s... | 56952b8e7d653355e138844ce91ea8db9d9c3b30 | 3,601,394 |
def fuzzy_match(s, choices, score_cutoff=80):
"""Fuzzy match string to list of possible values.
Based on http://pbpython.com/excel-pandas-comp.html
"""
m = process.extractOne(s, choices=choices, score_cutoff=score_cutoff)
if m:
return m[0]
else:
return nan | 2165bdce5cba5612d4aebcc47444cd68043149d9 | 3,601,395 |
def handle_formula_data(Y, X, formula, depth=0):
"""
Returns endog, exog, and the model specification from arrays and formula
Parameters
----------
Y : array-like
Either endog (the LHS) of a model specification or all of the data.
Y must define __getitem__ for now.
X : array-lik... | 38081650b99d1b917c6d986e6fc3ab2b9a8cd0b7 | 3,601,396 |
def draw_animation_layers(app, current_frame, img):
"""
Draw animation layers
:param app: generator app context
:param current_frame: array with number of current frames
:param img: target image
:return: new image and states array
"""
config = app.config
state = [False, False, False,... | f6bc4f8a59f3c108ab1182f76b959a05a7dbcef9 | 3,601,397 |
def generate_streak_matrix(weeks_data):
"""
Create the streak matrix
1 if the user committed
0 if the user hasn't committed
-1 to store null values
:param weeks_data: week-wise contribution data of the user
:return: matrix containing the values of the contribution streak
"""... | e6cc9e3c96aebb20ca8af11ae19e7f86aab96d62 | 3,601,398 |
def any_in_seq(search_vals, seq):
"""Check if any value in search_vals in the sequence seq"""
for v in search_vals:
if v in seq:
return True
return False | d314355aef2cba89833394ff6aeacf675daec7e6 | 3,601,399 |
from typing import List
from typing import Tuple
import itertools
def get_direction_permutations(number_of_particles: int) -> List[Tuple[Direction, Direction]]:
""" Returns all the possible direction permutations that can be obtained from a certain number of particles
>>> perms = get_direction_permutations(2... | d441b742281490d01b3925475c29d9a47f9932df | 3,601,400 |
def weeks_splitting_general(time1,data_list):
"""
Input:
time1: 1d numpy array, machine time since 1970
data_list: list of several 1d numpy arrays
Output:
unique_weeks_per_year: [[unique weeks for year1],..[unique weeks for year n],...]
times_per_week_per_year: [[times per week for... | f4638ca0571bc419ad3e0484f74d20a28ad6fc39 | 3,601,401 |
def fnrgf(
smap,
radial_bin_edges,
order,
attenuation_coefficients,
ratio_mix=[15, 1],
scale=None,
intensity_summary=np.nanmean,
intensity_summary_kwargs={},
width_function=np.std,
width_function_kwargs={},
application_radius=1 * u.R_sun,
number_angular_segments=130,
):
... | 36b160caa3fe18d06f795c84a3d5276148ff5fe6 | 3,601,403 |
def response(status, response_def):
""" Define a single response for the given handler.
You can define multiple responses for the given handler by using this
decorator multiple times.
"""
def decorator(fn): # pylint: disable=missing-docstring
meta = RouteMeta.load(fn)
meta.set_resp... | f8f5142ec7b4dde85a881b0f9262b5245d0832e4 | 3,601,404 |
import torch
def pseudo_beta_fn(aatype, all_atom_positions, all_atom_mask):
"""Create pseudo beta features."""
is_gly = torch.eq(aatype, rc.restype_order["G"])
ca_idx = rc.atom_order["CA"]
cb_idx = rc.atom_order["CB"]
pseudo_beta = torch.where(
torch.tile(is_gly[..., None], [1] * len(is_gl... | 2dcdf505a5e8a2d13d9fa0c0a4729ac057252ab7 | 3,601,405 |
def split_code_at_show(text):
"""
Split code at plt.show()
"""
parts = []
is_doctest = contains_doctest(text)
part = []
for line in text.split("\n"):
if (not is_doctest and line.strip() == 'plt.show()') or \
(is_doctest and line.strip() == '>>> plt.show()'):
... | a2676e233ac73bbb59061d264022fcace8406e87 | 3,601,406 |
def repr_value(value):
"""
Represent a value in human readable form. For long list's this truncates the printed
representation.
:param value: The value to represent.
:return: A string representation.
:rtype: basestring
"""
if isinstance(value, list) and len(value) > REPR_LIST_TRUNCATION... | 7541117982db6552202c70ca998fdd6618278521 | 3,601,407 |
def parse_test_list(test_list):
"""Parse user provided test list into internal format for test_runner.
Args:
test_list: A list of test classes/cases.
"""
result = []
for elem in test_list:
result.append(_parse_one_test_specifier(elem))
return result | ad239514d30e9587684a804ccf672239b708d50f | 3,601,408 |
def date2season(dt):
"""converts datetime to season like 2008/2009"""
y = dt.year
if dt.month <= 6:
return "%s/%s" % (y - 1, y)
else:
return "%s/%s" % (y, y + 1) | e8653264fb682c0c2e5ceeb0548f513e0af0eb33 | 3,601,409 |
def asHash(mobj):
"""Return a given hashCode for `mobj`, without caching it
This can be helpful in case you wish to synchronise `cmdx`
with a third-party library or tool and wish to guarantee
that an identical algorithm is used.
"""
handle = om.MObjectHandle(mobj)
return handle.hashCode() | fe8dd0c0934ae942d3a2b118add75cc5ab657224 | 3,601,410 |
import numpy
import scipy
def _c2g(timeaxis,coft):
""" Converts correlation function to lineshape function
Explicit numerical double integration of the correlation
function to form a lineshape function.
Parameters
----------
timeaxis : cu.oqs.time.TimeAxis
TimeAxis of the correlatio... | 6ce3d260a0de1228e523fa69943b07922e7e1b17 | 3,601,412 |
def Run(benchmark_spec):
"""Measure the sql performance in one VM.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
A list of sample.Sample objects with the benchmark results.
"""
vm = benchmark_spec.vms[0]
return hammerd... | 84a4d5a487759d4aff617c4bfb65dcade1d10f42 | 3,601,413 |
def limit(self, *args, **kwargs):
"""
Calls :meth:`.Table.limit` on each table in the TableSet.
"""
return self._proxy('limit', *args, **kwargs) | 40b22ac87d582d6e245bb3ed2ba6d60766ec0870 | 3,601,414 |
from datetime import datetime
def time_activation_domain(url):
"""Return time (in days) of domain activation."""
if url['host'].startswith("www."):
url['host'] = url['host'][4:]
pythonwhois.net.socket.setdefaulttimeout(3.0)
try:
result_whois = pythonwhois.get_whois(url['host'].lower()... | d048ae3ea00f419d84ceeb930540a0dc3efa6b3f | 3,601,415 |
def basic_cleaners(text):
"""Basic pipeline that lowercases and collapses whitespace without transliteration."""
text = lowercase(text)
text = collapse_whitespace(text)
return text | f9d9f102396128d8e8a6ca53b3356bf06395f172 | 3,601,416 |
import json
def get_json_file(path):
"""
Load a JSON file from disk.
"""
with open(path) as f:
data = json.load(f)
return data | acb099868c4baeb59ead76bb20018af477720609 | 3,601,417 |
def demask(data_masked, masks):
""" Fills the masked data with zeros.
Args:
data_masked: `float` of shape `[N_filtered, C]`,
masks: `bool`of shape `[N]`.
Returns:
`float` of shape `[N, C]`.
"""
C = tf.shape(data_masked)[-1]
masks = tf.reshape(masks, [-1])
shape = [tf.shape(masks)[0], C]
ids ... | 6507aea30dcddab22fbfeb075f24cdb647a3284f | 3,601,418 |
import dateutil
def _parse_common_date(date_str):
"""
:type date_str: str
:rtype: struct_time or None
"""
if date_str:
return dateutil.parser.parse(date_str)
return None | d4e1f26d66ed711dc7df9c199fdfc32f9b32709e | 3,601,419 |
def can_use_slay(user_id):
"""Return true if the user can use the command "slay"
Characters that can slay:
- All characters (to allow for fake claiming)
"""
return True | f8be6bd59c1c8bb8acb12d164c7b452c6ede1308 | 3,601,420 |
def make_parts(text, encoding=consts.SMPP_ENCODING_DEFAULT):
"""Returns tuple(parts, encoding, esm_class)"""
try:
# Try to encode with the user-defined encoding first.
encode, split_size, part_size = ENCODINGS[encoding]
encoded_text = encode(text)
except KeyError:
raise NotIm... | f1231a75c96cf0600216f0bc4f4379ac0fe94fa2 | 3,601,421 |
def cloud_eval(fen, **kwargs):
"""Wrapper for the `GET api/cloud-eval <https://lichess.org/api#operation/apiCloudEval>`_ endpoint."""
kwargs["fen"] = fen
return _api_get("/api/cloud-eval", kwargs) | af8feccaaf528a3a3e51b1f283da17b63000260a | 3,601,422 |
def get_emojis():
"""Retrieves hundreds of emoji glyphs derived from the UTF-8 character table.
Examples:
>>> moji = get_emojis()\n
>>> moji[1540:1547]\n
'🤠 🤡 🤢 🤣'
"""
emoji_list_1 = [chr(i) for i in range(127744, 127994)]
emoji_list_2 = [chr(e) for e in range(128000, 12... | 2ba1401ac5a2b326f56fc29fa91cd638e27c5426 | 3,601,424 |
def subjects_dewey(self, key, value):
"""Translates subject classification."""
_subjects = self.get("subjects", [])
_subjects.append({"scheme": "Dewey", "value": clean_val("a", value, str)})
return _subjects | c1db1f20418bfb517110b8857d490613beb8ff22 | 3,601,425 |
import itertools
def cmr_filter_urls(search_results):
"""Select only the desired data files from CMR response."""
if 'feed' not in search_results or 'entry' not in search_results['feed']:
return []
entries = [e['links']
for e in search_results['feed']['entry']
if 'li... | 403cfe92f6520af59a851b7b1913404be429a507 | 3,601,426 |
def measure_sidechain_torsion_angles(residue, verbose=True):
"""Calculates sidechain dihedral angles for a residue
Parameters
----------
residue : [ampal.Residue]
`Residue` object.
verbose : bool, optional
If `true`, tells you when a residue does not have any known
dihedral ... | 6809e692c1630dbfd58569074995dba1becb3acf | 3,601,427 |
from typing import List
from typing import Tuple
def residues(pdbfile: str, residues: List[int], buffer: float = 0.) -> Tuple[Tuple, Tuple]:
"""Generate a ligand autobox from a list of protein residues
The ligand autobox is the minimum bounding box of the alpha carbons of the
listed residues
Paramet... | 3ac4f9bd55fc58d342a37054405512f0722c3509 | 3,601,428 |
def roll_to_center(raw_transform, xc, yc):
"""
Shifts ("rolls") a two-dimensional numpy array so that the given coordinate will be in the center. Pixels
shifted over the edge re-appear on the other side
:param raw_transform: A numpy array to be shifted
:param xc: current x position in pixels
:p... | a2790ec03d6a4a912828e312361b8e69b78b50d6 | 3,601,429 |
import time
def generate_jsapi_pay_params(wx, prepay_id):
"""
生成微信公众号支付参数（WeixinJSBridge对象getBrandWCPayRequest参数）
:param wx: [dict]
:param prepay_id:
:return:
"""
params = {
'appId': wx['app_id'],
'timeStamp': str(int(time.time())),
'nonceStr': generate_random_key(1... | bfe9bea6820a30fdb9acf854c280cbad6abe4764 | 3,601,430 |
import math
def plot_binary_proportion_comparison(
series_names, values_by_category, title, xlabel, ylabel,
secondary_series_names=None, secondary_data=None,
tertiary_series_names=None, tertiary_data=None,
baseline_series_names=None, baseline_data=None,
raw_data_to_label_fn=str, show_ebars=True, z... | 778345ab57dabfb2c3d2d794e2e953dce2417538 | 3,601,431 |
def is_valid_instance_version(version):
"""True if string looks like an instance ID or a ref, or a tag."""
return (
is_valid_instance_id(version) or
is_valid_package_ref(version) or
is_valid_instance_tag(version)) | e51eb3fd129b401bcc1fdaaa44771f428a1e35b6 | 3,601,432 |
def _parse_config_file(filename):
"""Return plugin name and meta-data dict from plugin config file."""
parser = ConfigParser()
parser.read(filename)
name = parser.sections()[0]
meta_data = {}
for opt in parser.options(name):
meta_data[opt] = parser.get(name, opt)
return name, meta_... | 3802aa55b07749e2f452fd23c2acffc53e4118a7 | 3,601,433 |
def Floyd(G):
"""Returns the length of paths from all nodes to remaining nodes
Parameters
----------
G : graph
weighted graph
Returns
-------
result_dict : dict
the length of paths from all nodes to remaining nodes
Examples
--------
Returns the length of p... | 9fc9102225bf5273ff5a507b14ef2d8d679b2223 | 3,601,434 |
def merge(source, destination):
"""
run me with nosetests --with-doctest file.py
a = { 'first' : { 'all_rows' : { 'pass' : 'dog', 'number' : '1' } } }
b = { 'first' : { 'all_rows' : { 'fail' : 'cat', 'number' : '5' } } }
merge(b, a) == { 'first' : { 'all_rows' : { 'pass' : 'dog', 'fail' : 'cat', 'n... | aed6471ecce859c496b811ed81ea5f16b83b5c7d | 3,601,435 |
def transformer_decoder_attention_unit(x,
hparams,
encoder_output,
decoder_self_attention_bias,
encoder_decoder_attention_bias,
... | 2e07cc2aecc62a727f479c408fba52144d5a38f7 | 3,601,436 |
def isFileHandler(inSource, inTarget=None):
"""Source and target types can be either RegistrationPipeFH (or its base class)
or strings. Regardless of which is chosen, they must both be the same type.
If this function returns True - both types are fileHandlers. If it returns
false, both types are string... | 1c47e3af7d98fb9b802a90b372dce71c5ad76bff | 3,601,437 |
def _skip_node_of_type(node: Node, node_ops_to_skip: list):
"""
Skips nodes of specified ops starting from node 'node'.
:param node: node to start skipping Identity nodes.
:return: node of the op
"""
# skip the Identity node
while len(node.out_edges()) == 1 and node.op in node_ops_to_skip:
... | 904f4973f5d314786aea7408d83534ccef90318b | 3,601,439 |
def multiple_auth_methods(self):
"""ClickHouse SHALL reject Kerberos authentication if other
auth method is specified for user alongside with Kerberos.
"""
ch_nodes = self.context.ch_nodes
config_path = f"kerberos/configs/{ch_nodes[0].name}/users.d/kerberos-users.xml"
def modify_file(root):
... | bd35a3e9c620af8ca8ff05c4823f2df83a4ca7f3 | 3,601,440 |
def make_encoder_network(x, code_size):
""" Define the network mapping text vectors to embeddings """
x = keras.layers.Conv1D(
filters=64, kernel_size=3, strides=2, activation="linear"
)(x)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.LeakyReLU()(x)
x = keras.layers.Conv1D(... | 67c6b46bad29861b913c42ca34862224b75af9fd | 3,601,441 |
def format_datetime(session):
"""Convert date or datetime object into formatted string representation.
"""
if session.data is not None:
date_format = session.field.opts.date_format
if date_format == 'iso8601':
session.data = session.data.isoformat()
else:
sess... | 53a99843e47dde6b82cb48e77fd553bbf65dd646 | 3,601,442 |
def kde_max(kde, tol=1e-3):
"""Determine the maximum value of a KDE
INPUT: scipy.stats.kde object"""
# 2010-07-23 11:14 IJC: Created
cenguess = np.median(kde.dataset, 1)
init_steps = np.std(kde.dataset, 1)
max_vals = np.zeros(kde.d)
for dim in range(kde.d):
this_step = init_steps[dim... | 3dae757fa64f7e6a90b761cc966e2cb0fdb74770 | 3,601,443 |
from bs4 import BeautifulSoup
def UsaScraping():
"""
Take all USA page urls
"""
usa_url = "https://univ.cc/states.php"
usa_req = Request(usa_url, headers={'User-Agent': 'Mozilla/5.0'})
usa_webpage = urlopen(usa_req).read()
usa_soup = BeautifulSoup(usa_webpage,... | add4ce326b9f5e7599ff686605e09e7d9c428fb2 | 3,601,444 |
def get_pipeline_output_for_equity(df, symbol, drop_level=False):
"""
pipeline output contains many equities, if you want to view the pipeline for jsut one equity
you could use this method which slices a multiindex df (dates and equities)
:param df:
:param symbol:
:param drop_level: if True it w... | 86cb4a94d99ea32b42cdb868dea3cf4246ffe381 | 3,601,445 |
def _get_package_fields_from_configuration():
"""Get package fields from package field config dir."""
env_locals = {}
env_locals.update(metadata_validator.VALIDATOR_LOCALS)
env_locals.update(metadata_callback.CALLBACK_LOCALS)
configs = util.load_configs(
setting.PACKAGE_FIELD_DIR,
en... | 006b1b296a4b3121b42992f33b6769311ebb2272 | 3,601,446 |
def train_step(model, optimizer, loss_fn, conditions, true, out):
"""One training step
Args:
model: the feed-forward network
optimizer: the optimizer for the network
loss_fn: the loss function
conditions: the observing conditions used as inputs
true: the true galaxy ... | 8d9ba730582e1d7992bf2bd2f8359f4531392645 | 3,601,447 |
def train_step(train_loss, x, t, depth_t):
"""
学習処理
"""
with tf.GradientTape() as tape: #勾配計算の明示的指示
preds = model(x, t)
# t = t[:, 1:]
# mask_t = tf.cast(tf.not_equal(t, 0), tf.float32)
# t = tf.one_hot(t, depth=depth_t, dtype=tf.float32)
# t = t * mask_t[:, :... | ac2c8141c5c7fa0ff9e398dae670d547d7b7ef33 | 3,601,448 |
import torch
def get_concentrated_mask(class_weights, topk):
"""
Returns a logical mask indicating the categories with the top k largest
probabilities, as well as the catogories corresponding to those with the
top k largest probabilities.
Parameters
----------
class_weights : torch.Tensor... | 8663c6e4d868eb2100684132ef95c59eee9b3560 | 3,601,449 |
import json
import random
from geolocate_world import *
from geolocate_US import GeoLocation
import sqlite3
def geolocate_reverse(latlon):
"""
given (lat, lon) returns reverse geo location information
import json
from geolocate_reverse import geolocate_reverse
result = geolocate_reverse((40.733104,-73.99... | 07fde2bea4ce74c0e6f1fbfbe9ccee0bdef5b8d9 | 3,601,450 |
def read_pickle_template(file_path: str,
col_date: list,
array_col: str,
date_col: str,
id_col: str,
some_feature: str,
one_layer: bool = True
):... | adaeaac303fa171df87b0f4b73f5fa743677fc63 | 3,601,453 |
def mni152_to_fsaverage(img, fsavg_density='41k', method='linear'):
"""
Projects `img` in MNI152 space to fsaverage surface
Parameters
----------
img : str or os.PathLike or niimg_like
Image in MNI152 space to be projected
fsavg_density : {'3k', '10k', '41k', '164k'}, optional
D... | 1abb5f80eb7b06e79b953dad47002d93fa369f19 | 3,601,454 |
from typing import Dict
from typing import Any
def robot_and_modules(
modules_only: Dict[str, Any], ot2_default: Dict[str, Any]
) -> Dict[str, Any]:
"""Structure of SystemConfigurationModel with robot and modules."""
modules_only["robot"] = ot2_default
return modules_only | 126965499a60ff2355eaae4241e8caf0d6066555 | 3,601,455 |
def get_drush_user_password_command(name, password):
"""
Construct the drush user-password command
Prior to Drush 9.x, the upwd command expects --password, newer versions of
Drush do not.
"""
if determine_drush_major_version() >= 9:
return "upwd %s '%s'" % (name, password)
return "upwd %s --password=... | 61fad60a0c1b9763158e8170c8f9e8b1d89d492d | 3,601,456 |
import re
def get_params(rule):
""" Returns params from the url
Args:
rule (str): the endpoint path (e.g. '/v1/data/<int:id>')
Returns:
(list): parameters from the endpoint path
Examples:
>>> rule = '/v1/random_resource/<string:path>/<status_type>'
>>> get_params(rul... | 05414d950a6a603ff79fa2efff3ff3fef1e375f2 | 3,601,457 |
def add_brach(user):
"""
This function creates a new branch and adds the branch to the database,
The branch will belong to class Branch.
"""
chatt_object = Chatt.query.filter_by(user_id=user).first()
if chatt_object is not None:
new_branch = Branch(user)
db.session.add(new_branc... | f4dfe310bb226f22247f448c55096db50efd4e69 | 3,601,459 |
def reset_var():
"""reset vars"""
return [], [], True | 3d78de5df772f60e0331a95d9ca14f46d1d36071 | 3,601,460 |
def get_verts_from_master_arr(master_array):
"""
Extracts vertex objects based on state transitions in master_array.
Args:
master_array (numpy array)
"""
cur_state = master_array[0, 6]
vertices = []
for index, row in enumerate(master_array[:]):
if row[6] != cur_state:
cur_state = row[6]
vertices... | 3b526dbf59a26fd14853eb22fe1da2840209e6e9 | 3,601,461 |
def upstream_or_distgit_path(
request, upstream_and_remote, distgit_and_remote, ogr_distgit_and_remote
):
"""
Parametrize the test to upstream, downstream [currently skipped] and ogr distgit
"""
return {
"upstream": upstream_and_remote[0],
"distgit": distgit_and_remote[0],
"o... | 6f94a44e95301398c495dff56f9e470b46b5c737 | 3,601,462 |
def find_subclass(base: type, name: str) -> type:
"""Find a named subclass of a base class.
Uses only the class name without namespace.
"""
class_candidates = [
klass for klass in all_subclasses(base) if klass.__name__ == name
]
if len(class_candidates) == 0:
raise RuntimeError(... | c99c1d7949d56c94cb3531af381c644b5f4e397d | 3,601,463 |
def metrics(y_pred, y_true):
""" Calucate evaluation metrics for precision, recall, and f1.
Arguments
---------
y_pred: ndarry, the predicted result list
y_true: ndarray, the ground truth label list
Returns
-------
precision: float, precision value
recall: float, re... | bb0dfb24c3d9ddd0ce1a94887e60fe4ef2a89763 | 3,601,464 |
def get_related_field(name, short_description=None, admin_order_field=None, admin_prefix='admin'):
"""
Create a function that can be attached to a ModelAdmin to use as a list_display field, e.g:
client__name = get_related_field('client__name', short_description='Client')
"""
as_link = name.startswit... | 58197aabc818c1a40dda233945dc65594c50774a | 3,601,466 |
def soft_quantile_normalization(x, f, axis=-1, **kwargs):
"""Applies a (soft) quantile normalization of x with f.
The usual quantile normalization operator uses the empirical values contained
in x to construct an empirical density function (EDF), assign to each value in
x its corresponding EDF (i.e. its rank d... | 0c43527413f1f9fa39c1c8ccc5a5023ca0cccd9b | 3,601,467 |
import random
def get_random_path(graph):
"""Get a random path from the graph as a list of nodes.
:param pybel.BELGraph graph: A BEL graph
:rtype: list[BaseEntity]
"""
wg = graph.to_undirected()
nodes = wg.nodes()
def pick_random_pair():
"""Get a pair of random nodes.
:... | 4e42d8bcc1ddf52e169041eef26b298dd707319a | 3,601,468 |
def is_pre_release(version):
# type: (str) -> bool
"""Figure out if a given version is a pre-release."""
try:
return SemanticVersion(version).is_prerelease
except ValueError:
return False | 6bc4772bbfe54a2cc0dfa75ad5b3f87052d8b935 | 3,601,469 |
import re
def process_line(line):
"""
Pre processes the given line.
:param line: line as str
:return: preprocessed sentence
"""
if line == u"":
return u""
# detect sentences
sentences = sentence_detector.tokenize(line)
# process each sentence
for sentence in sentences:... | ab51d9331561f7e0ab85bbc2059538d586848861 | 3,601,470 |
def DTRTAV( file_contents, data_type, regression_var ):
""" 'Do The Right Thing' using adapative-validated glmnet as the regression method.
Pick the right version of glmnet to use based on data type.
Args:
file_contents: Raw form-data from HTTP POST request.
data_type: Parameter of the sam... | 15a9838f1b3dd404d70bd7f76325cf35f65d0057 | 3,601,471 |
def schedule_adaptive_max_pool2d(_, outs, target):
"""Schedule definition of adaptive_max_pool2d"""
with target:
return topi.generic.schedule_adaptive_pool(outs) | df74a56fdc222c03b7720447fc0cf4d3f72b0151 | 3,601,472 |
def dotProduct(d1, d2):
"""
@param dict d1: a feature vector represented by a mapping from a feature (string) to a weight (float).
@param dict d2: same as d1
@return float: the dot product between d1 and d2
"""
if len(d1) < len(d2):
return dotProduct(d2, d1)
else:
return sum(... | 65892f62f3c9d1ec13a62427aaafa7831a8ee1e5 | 3,601,473 |
import math
def rotatePoint(centrePoint,point,angle):
"""Rotates a point around another centerPoint. Angle is in degrees.
Rotation is counter-clockwise"""
pointX, centrePointX = point.getX(), centrePoint.getX()
pointY, centrePointY = point.getY(), centrePoint.getY()
angle = math.radians(angle... | 59bb9cea7f803113796e7249f1aeb6cffc75929a | 3,601,476 |
def input_data():
"""Input data (as coming from the view layer)."""
return {
"metadata": {"title": "Test", "type": {"type": "test"}},
} | 7a8bb2b82ee4754b2ee8b535c76a23ca5695c237 | 3,601,477 |
def create_synt_cube(cube, psf, ang, plsc, dist=None, theta=None, flux=None,
verbose=False):
"""
"""
centy_fr, centx_fr = frame_center(cube[0])
np.random.seed()
if theta is None:
theta = np.random.randint(0,360)
posy = dist * np.sin(np.deg2rad(theta)) + centy_fr
... | 3f833764968cc2449f49cdf8d0df87026adc1d58 | 3,601,478 |
def _chk_format_is_for_single_channel(chk_format: str = DFLT_CHK_FORMAT):
"""
Returns if a struct format string is designated for a single channel of data
>>> assert _chk_format_is_for_single_channel('h') == True
>>> assert _chk_format_is_for_single_channel('@hq') == False
"""
return _chk_format... | b2d1f91b1847a7002c7f053230ddc6cea573945e | 3,601,479 |
def match_card(qCard, train_ranks, train_suits):
"""Finds best rank and suit matches for the query card. Differences
the query card rank and suit images with the train rank and suit images.
The best match is the rank or suit image that has the least difference."""
best_rank_match_diff = 10000
bes... | d1797ce279e1ec8a45d27bc0a5a29960bf87b032 | 3,601,480 |
import re
def remove_extra_space(text):
"""
Remove extra space between two words
Parameters
----------
text : str
Returns
-------
text: str
"""
return re.sub(' +', ' ', text) | d1b68128fe0655fe2f84263b0b9d6b83759552b1 | 3,601,481 |
def linter():
"""Fixture providing a testing linter."""
pylinter = UnittestLinter()
pylint_sqlalchemy.register(pylinter)
return pylinter | 3620d34a48b6ec675f47d83146fd9b6576563b2b | 3,601,482 |
def string_from_file(file_name):
"""Read contents of a resource file into a string
If the class has public attributes, they may be documented here
in an ``Attributes`` section and follow the same formatting as a
function's ``Args`` section. Good for planning using just docstrings.
:param str file_... | 8419740fd12a2cf37fab3b55610b6ccf9009e54a | 3,601,483 |
def bitvec_number(x):
"""Return number represented by given bitve"""
return sum(x << (32 * i) for i, x in enumerate(x.data[0:x.size])) | ad393d4f30d9fef35f089491a7c59faca3ec6610 | 3,601,484 |
def convolve(img, kernel):
"""Performs a naive convolution."""
if kernel.shape[0] % 2 != 1 or kernel.shape[1] % 2 != 1:
raise ValueError("Only odd dimensions on filter supported")
img_height = img.shape[0]
img_width = img.shape[1]
pad_height = kernel.shape[0] // 2
pad_width = kernel.sha... | 51bd2bd681fb404d9dbce745f46406f136f22090 | 3,601,485 |
def ddt_named(parent, child):
"""
Helper to get more readable dynamically-generated test names from ddt.
"""
args = RenamedTuple([parent, child])
args.__name__ = f'parent_{parent}_child_{child}' # pylint: disable=attribute-defined-outside-init
return args | 9772c35d56afbc1f4fb07d65e2d28662878cbb68 | 3,601,486 |
def garfield_empty_mock(url, request) -> str:
"""
Mock HTTP empty response using HTTMock
:param url: str
:param request: Request
:return: str
"""
return """
<html>
<body></body>
</html>
""" | c2c349e40f315bfc625680fc5b0e92a7a54f5f7c | 3,601,487 |
from urlparse import parse_qs
def callback():
""" Step 3: Retrieving an access token.
The user has been redirected back from the provider to your registered
callback URL. With this redirection comes an authorization code included
in the redirect URL. We will use that to obtain an access token.
""... | d49dc7f53763dedd64050fea746a489d67d90e34 | 3,601,489 |
def model_atmosphere(altitudes):
"""Model atmospheric pressure, temperature and speed of sound.
Args:
altitudes (np.array): 1-d array of geopotential altitudes in metres
Returns:
A triple giving corresponding 1-d arrays of estimated pressure,
temperature and speed of sound. Units a... | 1693a0501e6c6e804b67289dd7d3136d71d2a3fd | 3,601,490 |
def has_permissions(**perms):
"""
Same as the default discord.ext.commands.has_permissions
except this one supports overriding perms
"""
def predicate(ctx):
if ctx.override_perms:
return True
ch = ctx.channel
permissions = ch.permissions_for(ctx.author)
... | 4beafbae218d3fc6ac88215b8d94b27b46a26099 | 3,601,491 |
def check_order(order):
"""
Checks the specified drawing order is valid and returns the corresponding
tree traversal order.
"""
if order is None:
order = "minlex"
traversal_orders = {
"minlex": "minlex_postorder",
"tree": "postorder",
}
if order not in traversal_o... | c8478bbbb59ce25beec4a4196dc183a49ecb62ba | 3,601,492 |
def get_total_num_women(dataframe, fil):
"""Counts total women in category
Returns:
[list] -- total number of women, total number in category
"""
total = 0
matched = 0
for index, row in dataframe.iterrows():
if fil(row):
total += 1
if row["gender"] == "F"... | 478046f1f7bb62280f36df50b50c537dba2eb063 | 3,601,493 |
def get_cat2id(item_metas, n_entities):
"""Extracts all categories from item metada and maps them to an id"""
categories = set([cat for it_meta in item_metas for cat in it_meta.categories])
return {cate: n_entities + i for i, cate in enumerate(categories)} | 38c7895949d3eccf9d8d4fc6c609b036700f93d8 | 3,601,494 |
def flatten_weights_and_state(weights, state):
"""Flatten weights and state into lists, excluding empty and cached ones."""
def _is_empty_weight(x):
return (x is EMPTY_WEIGHTS or
(isinstance(x, dict) and x == GET_WEIGHTS_FROM_CACHE))
flat_weights = [w for w in fastmath.tree_flatten(weights)
... | 4e25fe154e27315ecab6f800d3f0ad800c33cd7d | 3,601,495 |
def add_acids(EP, amino_acids):
"""
The function check that all alpha amino acids in sequence,
else add this monomers.
Parameters
----------
EP : dict
Variants of peptide sequences.
amino_acids : list
List of alpha amino acids.
Returns
-------
EP : dict
C... | 45027c8b38c97da4af677e79af0c302539d8b747 | 3,601,496 |
def average_gradients(tower_grads):
"""Calculate the average gradient for each shared variable across all towers.
Note that this function provides a synchronization point across all towers.
Args:
tower_grads: List of lists of (gradient, variable) tuples. The outer list
is over individual gradients... | b7fecfb9ad602c3c388e78fc08b8945351583909 | 3,601,497 |
def winnforum_testcase(testcase):
"""Decorator for common features (e.g. logging) for WinnForum test cases."""
def decorated_testcase(*args, **kwargs):
if not testcase:
raise ValueError('Avoid using @winnforum_testcase with '
'@configurable_testcase')
_log_testcase_header(testca... | 9bc212ac433eec09cfc283edf5f272b7860d81c8 | 3,601,498 |
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