content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def layout_photo_5_2(photo):
"""
在5寸照片上排版2寸照片
:param photo: 待处理照片2寸
:return: 处理后的照片
"""
bk = Image.new("RGB", (HEIGHT_5IN, WIDTH_5IN), (255, 255, 255)) # 竖版排版
# 创建画笔
draw = ImageDraw.Draw(bk)
draw.line([(0, WIDTH_5IN / 2), (WIDTH_5IN, WIDTH_5IN / 2)], fill=128) # 横线
draw.line([... | 2183babdc363620c7126df810c6d7e85fdbdebe0 | 3,598,954 |
from typing import Set
def get_gt_nsrts(predicates: Set[Predicate],
options: Set[ParameterizedOption]) -> Set[NSRT]:
"""Create ground truth NSRTs for an env."""
if CFG.env in ("cover", "cover_hierarchical_types", "cover_typed_options",
"cover_regrasp", "cover_multistep_opti... | 1fa376eef49e0ac09b89c0a3ccfc096edd159779 | 3,598,955 |
import zlib
import struct
def fragments_to_schedule(fragments: list) -> dict:
"""Convert a set of fragments (a blob) into a schedule.
May raise a `zlib.error` exception.
"""
raw_schedule = zlib.decompress(bytearray.fromhex("".join(fragments)))
zone_idx, schedule = None, []
old_day, switchpo... | b9d436a539675333c24d174c0d7a309438b28ee4 | 3,598,956 |
def compute_scl_mask(
scl, mask_vals, gdf, verbose=False,
verbose_plot=False, scene_id=None, res=None, date=None, bins=100):
"""Compute the Red Chlorophyll Index image from band08 and band04 values
Args:
scl (dict): Sentinel-2-L2A Band04 raster data
mask_vals (dict): Sentinel-2-... | 6c19a3d188a5bff07712b6e926ece7c02c08ad21 | 3,598,957 |
def r_value(aa1, aa2, aa_dict, matrix):
"""
:param aa1:
:param aa2:
:param aa_dict:
:param matrix:
:return:
"""
return sum([(matrix[i][aa_dict[aa1]] - matrix[i][aa_dict[aa2]]) ** 2 for i in range(len(matrix))]) / len(matrix) | 034d1d7eb0baa63cd5bd4572c87f86bcc8774de4 | 3,598,958 |
def init(param_test):
"""
Initialize class: param_test
"""
# initialization
default_args = ['-t mt/label/ -w mt/warp_template2mt.nii.gz -gm mt/mt1_gmseg.nii.gz -wm mt/mt1_wmseg.nii.gz -manual-gm mt/mt1_gmseg_goldstandard.nii.gz -sc mt/mt1_seg.nii.gz -param step=1,type=seg,algo=centermassrot,metric=M... | cd0b7e61cabda521c0f07e72e74316e7766ea2d2 | 3,598,959 |
def compose_all(graphs, name=None):
"""Return the composition of all graphs.
Composition is the simple union of the node sets and edge sets.
The node sets of the supplied graphs need not be disjoint.
Parameters
----------
graphs : list
List of NetworkX graphs
name : string
S... | 0dd2a9f4e846cc7a7410ecc04cb1f85e2776bbe9 | 3,598,960 |
def create_Q_scipy(par,sol,ast,solmethod):
""" create Q for use with scipy """
def remove_small(x):
I = np.abs(x) < eps_low
y = x.copy()
y[I] = 0
return y
for iz in range(par.Nz):
#order of diagionals is important to getsorted indices
ast.Qs[iz] = diags( di... | 4172f8c116803d2a0f030f442f647c56f67478d6 | 3,598,961 |
def init_standard(nnet, dataset):
"""My standard initialization for a neural network """
# use orthogonal initialization of all W's
for synapse in nnet.synapses:
W_start = synapse.W
shape = B.eval(W_start.shape)
W_final = orthogonal(shape)
B.set_value(W_start, W_final)
... | 882e0b886bb17877983404b3aa8232e9c499d674 | 3,598,965 |
def calculateProgressMetrics(job):
"""
job has the most recent values updated for the current event being processed. Calculate status
and progress metrics using the new information.
:param job: JSON job data struture from dynamodb
"""
progressMetrics = {}
# BASE TIMES FROM EVENTS
# cr... | 73524e53f5dd77c81cd0e9f6239588e41b9bc841 | 3,598,966 |
def _ydiff_excess_equals_expected(yint, xmin, xmax, x, model):
"""The ExcessEqualsExpected method to compute Y (differential).
y / yint = y_model / yint_model"""
yint_model = _integrate(xmin, xmax, model)
y_model = model(x)
return y_model * (yint / yint_model) | 8476fee5e720ddb45a46a9baa61b18dc235f27bc | 3,598,967 |
def rescale_boxes(boxes, current_dim, original_shape):
""" Rescales bounding boxes to the original shape """
orig_h, orig_w = original_shape
# The amount of padding that was added
pad_x = max(orig_h - orig_w, 0) * (current_dim / max(original_shape))
pad_y = max(orig_w - orig_h, 0) * (current_dim / m... | 769264b54e0a2dc438d78942e4c86f46c1b6470e | 3,598,968 |
def get_summary_indices(df, on='NOSC'):
""" Get the summary stats for the indices: median, mean, std, weighted mean and weighted std """
samples = get_list_samples(df)
samples.append(on)
t = df[samples]
t = t.melt(id_vars = [on], var_name = 'SampleID', value_name = 'NormIntensity')
t = t[t[... | 34549141bdcd7b4092748c540275d4cea4ead9cf | 3,598,969 |
def get_eqopp_policy_from_selection_rate(selection_rate,
loan_repaid_probs,
pis,
scores):
"""Return policy with specified selection rate on Y=1 for both groups."""
# compute P(X, Y=1|A)
... | bffa9b5f3d1715b21422074b1c1b4d8c08fe6583 | 3,598,970 |
def getWell(mmdict, df, size, row, column, nchannels, channels=None):
"""
Get every image patch in a well
Parameters
----------
mmdict : dict
dictionary of memmap file
df : DataFrame
size : int
size of image patches
row : int
row in the plate
column: int
... | 4814e8b8ab636691486dc7fd73077b9a84bc403a | 3,598,971 |
def concat_time_series_matrices(mats, t=None):
"""Concatenate a list of gene x cell matrices into a single matrix.
Arguments
---------
mats: :class:`~numpy.ndarray`
A list of gene x cell matrices. The length of the list equals the number of time points
t: :class:`~numpy.ndarray` or list
... | afca744f7bbb945038efa59c36d44243ac5dbaa2 | 3,598,972 |
def get_route_config(route, ifname):
"""
Builds a basic route config dictionary with all of the fields required to
format a basic network_route puppet resource.
"""
if route['prefix']:
name = '%s/%s' % (route['network'], route['prefix'])
else:
name = 'default'
netmask = IPNet... | e9400f52c2647a44e88932b2d808c6f18b4169ce | 3,598,973 |
def concat (tensors_list, axis):
"""
Better version.
"""
if len(tensors_list) == 1:
return tensors_list[0]
return tf.concat(tensors_list, axis) | f0316f688ff0cc3a0d18970a9d614d48db584e89 | 3,598,974 |
def redundant(src, key=None, distinct=False, sort=True):
"""The complement of unique(), returns non-unique values. Pass
distinct=True to get a list of the *first* redundant value for
each key. Results are sorted by default.
>>> redundant(range(5))
[]
>>> redundant([1, 2, 3, 2, 3, 3])
[[2, 2... | 3b660e5ba3833c95bba97e4546b7915d6f0343ae | 3,598,975 |
from datetime import datetime
def serialize(data, maximum_depth=0, level=0):
"""
Serializes dicts and django models to dict
:param data: The data
:param maximum_depth: Maximum recursion depth
:param level: Current level
:return: A dict
"""
result = None
if isinstance(data, dict):
... | 48cd8a5dad8d4a200be9f56e66faf0421052dd03 | 3,598,977 |
def create_embeddings_dic(all_words, embeddings_path, num_dimensions):
"""
:param all_words:
:param embeddings_path:
:param num_dimensions:
:return:
"""
embeddings_dic = {}
print("Building embeddings dic from " + embeddings_path)
with open(embeddings_path, "rb") as infile:
ne... | 6a621df4d55ec8c7151fbe8d105b98787414b559 | 3,598,978 |
def user_can_view_data(user):
"""
Determine if the user is active and authenticated.
:param user: The user to lookup.
:type user: str
:returns: True, False
"""
if user.is_active:
if user.has_access_to(GeneralACL.WEB_INTERFACE):
return user.is_authenticated()
else... | 34849adc4f87892711e52542e5142a6d9fd27df7 | 3,598,979 |
def most_similar_command(command: str, command_list: set) -> (list, str):
"""
Determine whether its possible to suggest a similar command.
The similarity is determined by the levensthein distance and a factor (currently 1/3)
sets a limit where a similar command is useful to be automatically used. If the... | 58fe2cca6b7194745274e6e873cde0ca2e4980fa | 3,598,980 |
import torch
def precision_score(tp: torch.Tensor, fp: torch.Tensor, reduce: bool = True) -> torch.Tensor:
"""Computes the precision using true positives and false positives.
:param tp: true positives, dims (C,) or ()
:type tp: torch.Tensor
:param fp: false positives, dims (C,) or ()
:type fp: tor... | 33ba13fbf4a267c43d32b440e5aaab317e671e50 | 3,598,981 |
def gen_cp(duration, h):
"""
generate the CP times from the trial by successively sampling
from an exponential distribution
:param duration: trial duration in sec
:param h: hazard rate in Hz
:return: numpy array of change point times strictly positive and inferior to duration
"""
cp_time... | 4975795a9726e73987fccd0af65aa9f6430c1005 | 3,598,982 |
import socket
def tcp_socket_open(host, port):
"""
Returns True if there is an open TCP socket at the given host/port
"""
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.settimeout(1)
try:
return sock.connect_ex((host, port)) == 0
except socket.timeout:
return... | 11519e5d9f2d1de39d8091af011f0458e556021c | 3,598,983 |
def make_color_wheel() -> np.ndarray:
"""
Create colour wheel.
Code adapted from https://github.com/liruoteng/FlowNet/blob/master/models/flownet/scripts/flowlib.py
"""
red_yellow = 15
yellow_green = 6
green_cyan = 4
cyan_blue = 11
blue_magenta = 13
magenta_red = 6
ncols = re... | 71e64dd033164ce6541e67365583f7d0d5d2fd7e | 3,598,985 |
def get_geo_node_us(dirname, points, state, county, simplify):
"""
This function maps a each node to a geographic region. For US only.
"""
# Directories
shapepath = dirname + '/resources/additional_info/state_'+ state + '/cb_2015_'+state+'_tract_500k'
# Read in shapefile
sf = shapefile.R... | 33c3fb8073d2faae5faf1b632e7a64c9afe09355 | 3,598,986 |
def transparently(function):
"""
Wrap ``function`` so that it is called, but that the first parameter is
returned.
"""
@wraps(function)
def decorated(value, *args, **kwargs):
function(value, *args, **kwargs)
return value
return decorated | 30a0bc37093b18a36edf5b910342b229349e6582 | 3,598,988 |
import re
def is_relative_import_name(import_name: str) -> bool:
"""Checks if an import name is relative."""
return re.fullmatch(_RELATIVE_IMPORT_NAME_REGEX, import_name) is not None | 428925346bf0f710a886bc7b05174321142601a8 | 3,598,989 |
def decode_html(html, default_encoding=DEFAULT_ENCODING, encoding=None, errors=DEFAULT_ENC_ERRORS):
"""
Converts a `html` containing an HTML page into Unicode.
Tries to guess character encoding from meta tag.
"""
if isinstance(html, unicode):
return html
if encoding:
return html... | 7dbb2b71a24c3a7662f2351eba516f6f0c518bb3 | 3,598,990 |
def variance(data):
""" Assumes that an array of integers is passed """
mean = sum(data)/len(data)
tot = 0.0
for d in data:
tot += (d - mean)**2
return tot/mean | 3329792cf7043ad6e68cad39d6a07bf2909d1496 | 3,598,991 |
def generalized_parity_solver_with_partial(g, partial_solver):
"""
Generalized parity games solver. This is an implementation of the classical algorithm used to solve generalized
parity games. This is the wrapper function which complements every priority and calls the actual algorithm.
:param g: the gam... | 142424d5f315cdb7e9fd1f6c2fbbf4f44493aacc | 3,598,993 |
def susceptible_60():
"""
Real Name: b'Susceptible 60'
Original Eqn: b'INTEG ( deimmunization rate 60-infection rate 60, init Susceptible 60)'
Units: b'person'
Limits: (None, None)
Type: component
b''
"""
return _integ_susceptible_60() | 0d180ea1a1523e6ee748bcf8f9e283d5e2bde2f2 | 3,598,994 |
def encode_track_results(track_results):
"""Encode bitmap mask to RLE code.
Args:
track_results (list | tuple[list]): track results.
In mask scoring rcnn, mask_results is a tuple of (segm_results,
segm_cls_score).
Returns:
list | tuple: RLE encoded mask.
"""
... | 371fcc456cfa21c34a9c8dab95f90b9b4f2ec7f9 | 3,598,996 |
def linear_activation_backward_reg(dA, cache, activation_fn="relu", lambd=0):
"""
Arguments
---------
dA : 2d-array
post-activation gradient for current layer l.
cache : tuple
values of (linear_cache, activation_cache).
activation : str
activation used in this layer: "sig... | a1075628dfc62969d981c4537e5b44fd44b5f461 | 3,598,997 |
import traceback
def get(key, response=None):
"""Returns k:str to exist in the yml file, Otherwise it returns default."""
try:
global CONF_FILE
if CONF_FILE:
if isinstance(key, str):
sb = key.strip('.').split('.')
p = CONF_FILE
for i... | f36c0fd787966c63ce2b2f3cf1b397ffceb8f4a5 | 3,598,998 |
from typing import Union
from typing import List
from typing import Optional
from typing import Dict
from pathlib import Path
import torch
def train_model_pretraining(
dataset_name: Union[str, List[str]] = None,
dataset_sampling: Union[float, List[float]] = None,
dataset_dir: str = None,
... | dd7d445e7fc1193a9e1f77086ee06f1f31784eda | 3,598,999 |
def poor_cond_toep(n):
"""
:param n: size of output
:returns: the top row of a randomly scaled PSDT matrix whose
:math:`L^2` condition number scales exponentially with `n`
"""
top = np.arange(n)[::-1] * (np.random.rand() + 1e-3)
while smallest_eig(top) < 0:
top[0] *= 2
... | f9645e50ade7aa76e694242fd61a4eb1bae8a443 | 3,599,000 |
from datetime import date, timedelta
from numpy import ndarray, matrix, generic
from pandas import DataFrame, Series, Index
from spyder.pil_patch import Image
def get_supported_types():
"""
Return a dictionnary containing types lists supported by the
namespace browser.
Note:
If you update this li... | 51e577fbfbe97f5fc945fb906399a8ab7a5e188c | 3,599,001 |
def view_mempool():
"""insert new transaction to mempool"""
return mempool.transactions | a8dbb0fd7b50a6f89c895c641aadc854e195643b | 3,599,002 |
def calculate_profile_input_for_cluster_analysis_from_ERA5(p_profile, t_profile, td_profile, q_profile,
u_profile, v_profile, h_profile, surface_p):
"""
takes data from ERA5 for only one time stamp for all pressure levels from 250 to 1000 hPa
:param... | dd1f15c3b62ad5e6b9706b59c55fcb220340b9a0 | 3,599,003 |
def components_graph(geo, stereo=True):
""" Generate a list of molecular graphs where each element is a graph that
consists of fully connected (bonded) atoms. Stereochemistry is included
if requested.
:param geo: molecular geometry
:type geo: automol geometry data structure
:... | e1fbee2042601620f342578725d2eb35fc6bc7bf | 3,599,004 |
from pathlib import Path
import json
def get_configured_hosts():
"""Look into the user preferences to enumerate the remote ssh hosts
that were configured for use with dallinger.
"""
hosts_dir = Path(APPDIRS.user_data_dir) / "hosts"
res = {}
if not hosts_dir.is_dir():
return res
for... | 49a7c8b4aa7ad34ad82bc5df5c196de957495fa1 | 3,599,005 |
import numpy
def readValidationImages():
""" Reads in all images in the validation directories
Creates a numpy array containing the pixel data of all the images in the directories of validation images. Assumes that the subdirectories "PosVal" and "NegVal" exit in the working directory
Args: None
... | b110d5e20091fd09b6ef9c3cd7ca540933e3451a | 3,599,006 |
from typing import Optional
from typing import List
import math
import random
def sample_from_range(range_max: int,
sample_ratio: float,
max_samples: int,
preselected: Optional[List[int]]) -> List[int]:
"""
Given a range of numbers in 0..range_... | 8be7b5ded3b6f3b54da57027a5d7629a9bf5dc9f | 3,599,007 |
import regex
import traceback
def data_masking(instance, db_name, sql, sql_result):
"""脱敏数据"""
try:
keywords_count = {}
# 解析查询语句，判断UNION需要单独处理
p = sqlparse.parse(sql)[0]
for token in p.tokens:
if token.ttype is Keyword and token.value.upper() in ['UNION', 'UNION ALL... | b92df933e5681cf729a213dd7bc92eda5962c80d | 3,599,008 |
from typing import List
def filter_single_group(
molecules: pd.DataFrame, groups: List[str], exclude: bool) -> pd.DataFrame:
"""Check that the molecule has a single functional group."""
# First filter the molecules that have the target functional groups
molecules = filter_by_functional_group(molec... | 31fb07a77282c317f577ca2c0c8c935887af5894 | 3,599,009 |
def occ(s1,s2):
"""occ (s1, s2) - returns the number of times that s2 occurs in s1"""
count = 0
start = 0
while True:
search = s1.find(s2,start)
if search == -1:
break
else:
count +=1
start = search+1
return count | 4f107e5552d794e9e82a8a6b19ec63992034e9f7 | 3,599,010 |
import functools
import operator
def prod(x):
"""Equivalent of sum but with multiplication."""
# http://stackoverflow.com/a/595396/1088938
return functools.reduce(operator.mul, x, 1) | 31f4defa3401d6dcf05f8ec70c32650f000a1852 | 3,599,011 |
def compare_dfs(old_df, new_df, on):
"""
Function to compare two DataFrames with nans and return a dict with rows that have changed (diff), rows that exist in new_df but not in old_df (new), and rows that exist in old_df but not in new_df (remove).
Both DataFrame must have the same columns.
Parameters... | 705f19b9b48827dca3baf4e31e6396f706d9136c | 3,599,012 |
def indent(text, prefix=" "):
"""
Add a prefix to every line in a string.
"""
return "\n".join(prefix+line for line in text.splitlines()) | e83cc0b14c5b8c304f6e41bf145b06b1de451e8c | 3,599,013 |
def load(fname):
"""Return a `BDD` loaded from DDDMP file `fname`.
If no `.orderedvarnames` appear in the file,
then `.suppvarnames` and `.permids` are used instead.
In the second case, the variable levels contains blanks.
To avoid blanks, the levels are re-indexed here.
This has no effect if `... | d81ff16bb356f00df3e795991657772ee71ca2d5 | 3,599,014 |
def get_iterator(ip, value, community_string, is_oid=False):
"""
Get pysnmp iterator
Args:
ip: The device's IP address
value: MIB id or SNMP OID
community_string: The community string for the network. Usually 'public' or 'private'
is_oid: boolean if t... | f1fcfdb3aa772efd7448c1dec3506aa14d205731 | 3,599,015 |
from typing import Tuple
from typing import Dict
from typing import Union
def _get_event_description(
elem: etree.Element,
) -> Tuple[str, Dict[str, Union[str, int]]]:
"""Returns the full description of a single XML Event in the DFL format.
Parameters
----------
elem: lxml.etree.Element
l... | 8e1af0808d570f6ce1949b8035dcb316f12c6a70 | 3,599,016 |
def deserialize(fmt, stream_or_string, **options):
"""
Deserialize a stream or a string.
"""
d = get_deserializer(fmt)
return d(stream_or_string, **options) | f14c8b42f2be5a2c3301cbe1ca7bf70fe22ddc73 | 3,599,017 |
import logging
def read_nitfile(path: str):
""" read and parse the "nit" (config) file and return as a dict """
logging.info(f"opening nit file {path}")
nit = {}
with open(path) as fp:
for line in fp.readlines():
if '#' in line:
line, comment = line.split('#', max... | 831453ee9e0bc6b4c58969ffad10cd385754fdc0 | 3,599,018 |
def get_resblock(model, layer_name):
"""
model is a class resnet
e.g. layer_name is "layer1.0.relu2"
"""
obj_name, b_idx, act_name = layer_name.split(".")
b_idx = int(b_idx)
block = getattr(model, obj_name)[b_idx]
return block | a4ae6c65df336b3dd36b53e9dd348a76b4a47b89 | 3,599,020 |
def compute_rolling_mean(df, window=30):
"""Return rolling mean of given values, using specified window size."""
df['SMA_{}'.format(str(window))] = df['Price'].rolling(window=window, center=False).mean()
return df | 36048112a720e0bdd66d2dfeeac6fb5cab62fa46 | 3,599,021 |
def fix_link_header(headers):
"""Replace the GitLab URL in the Link header."""
if 'Link' in headers:
headers['Link'] = headers['Link'].replace(
app.config['GITLAB_URL'], app.config['HOST_NAME']
)
return headers | d11d1cde6a474084a208d82d31f68d3b0b3eb44b | 3,599,022 |
def insert(linked_list, value):
"""Metoda insert() vlozi na konec seznamu linked_list (za prvek last)
novy uzel s hodnotou value. Vraci nove vlozeny objekt.
"""
n = Node()
n.value = value
n.prev = linked_list.last
if linked_list.first is None:
linked_list.first = n
else:
... | 82da5a2180e83d6dd66e0125ecde456bb53e6aaa | 3,599,024 |
import torch
def compute_loss(leo, metadata, train_stats, transformers, mode="meta_train"):
""" Performs meta optimization across tasks
returns the meta validation loss across tasks
Args:
metadata(dict): dictionary containing training data
train_stats(ob... | ef299d9c4de41c9c97bea37f2606418e0aaeacec | 3,599,025 |
def GetAcceleratorFlag():
"""Gets the --accelerator flag."""
help_text = """\
Manage the configuration of the type and number of accelerator cards attached.
*count*::: The number of pieces of the accelerator to attach to the allocation.
*type*::: The specific type (e.g. nvidia-tesla-k80 for nVidia Tesla K80) of
a... | b501f4fc87dea568575494a246cbc9f6f5ba940c | 3,599,026 |
def _format_A_constraints(A, n_x, sparse_lhs=False):
"""Format the left hand side of the constraints to a 2D array
Parameters
----------
A : 2D array
2D array such that ``A @ x`` gives the values of the upper-bound
(in)equality constraints at ``x``.
n_x : int
The number of v... | 0f5f6ce6163fde35254d9bfdb6a8f3ccad7914b8 | 3,599,027 |
def argsort(x, topn=None, reverse=False):
"""Get indices of the `topn` smallest elements in array `x`.
Parameters
----------
x : array_like
Array to sort.
topn : int, optional
Number of indices of the smallest(greatest) elements to be returned if given,
otherwise - indices o... | 6fcd896d3bbf296f818e2dcd993f75583ba880de | 3,599,028 |
def preprocess_image(image_path: str) -> np.ndarray:
"""
Pre-processing steps:
* Load the input image in grayscale mode (1 channel),
* resize it to 224x224 pixels for the VGG19 CNN model,
* transform it to an array format,
* normalise the pixel intensities.
:param image_path:... | 2b674415e6b096381a3b3c918be46f6a86545de7 | 3,599,029 |
from typing import Sequence
def is_planar_face(face: Sequence[Vec3], abs_tol=1e-9) -> bool:
""" Returns ``True`` if sequence of vectors is a planar face.
Args:
face: sequence of :class:`~ezdxf.math.Vec3` objects
abs_tol: tolerance for normals check
"""
if len(face) < 3:
ret... | a50dcf37bec0e1c8a7a210195676ef5bdf6aff21 | 3,599,030 |
def is_campaign_config_file(file_name):
"""
Check if the given file is a CampaignConfig
:param file_name: string
:param file_name: File to check
:rtype: boolean
:return: If the file is a campaign config or not
"""
status = False
try:
# MUST BE xml file
if file_nam... | 57c9314af84ea1835fb16db54ce53d9997541254 | 3,599,032 |
def dice_coefficient(prediction, ground_truth, smooth=1.0):
"""
Function to calculate the dice coefficient.
:param prediction: the logits (before softmax)
:param ground_truth: the segmentation ground truth
:param smooth: Smoothing coefficient.
:return: the dice coefficient.
"""
y_true_f... | 629d083edaa386bb6527ed498c5f4f76cd8efaa7 | 3,599,033 |
def bilinear_interpolate(data, x, y, nan_boundaries=False):
"""
Author: Michael Denbina (source: Kapok library)
Function to perform bilinear interpolation on the input array data, at
the image coordinates given by input arguments x and y.
Arguments
data (array): 2D array containing raster ... | 5a059a31d135c3947894aa80d3b8c3d097ae5337 | 3,599,034 |
def get_data(col='close'):
""" Returns a 3 x n_step array """
msft = pd.read_csv('data/msft.csv', usecols=[col])
aapl = pd.read_csv('data/aapl.csv', usecols=[col])
ibm = pd.read_csv('data/ibm.csv', usecols=[col])
# recent price are at top; reverse it
return np.array([msft[col].values[::-1],
... | 7bc9d759b2037cdb45db8da619d8cfab05f7ec0f | 3,599,035 |
def GetCommentsForParams(func):
"""Gets the comments for the params.
Args:
func: The function.
param: The parameter.
Returns:
a (string, dict) pair. The string is the comments minus the param parts.
The dict is a dict of param names to comments.
"""
collecting_key = None
param_comments = {}... | c81cc9ff3b8fb6da57c257d7a8b536cafa22bad2 | 3,599,036 |
def FindClientNode(mothership):
"""Search the mothership for the client node."""
nodes = mothership.Nodes()
assert len(nodes) == 2
if nodes[0].IsAppNode():
return nodes[0]
else:
assert nodes[1].IsAppNode()
return nodes[1] | 06ac3bcde5571ca425d47885ad5839f2aa32ca0d | 3,599,037 |
def index_max_maxunpooling(prev_indexes,X,pool_size,stride):
"""Function that return the indexes of maximum values of maxpooling
operation.
Args:
prev_indexes: Python list with tuples of indexes in the form
(batch, frame, height, width, channels).
X: Input of the max... | 80f0c1ad85a97c576608912af3d46c6a1300a654 | 3,599,038 |
import textwrap
def to_flatimage(pathfile, to="(-3,0,0)", width=8, height=8, name="temp"):
"""
Adds a flat image to the canvas.
"""
text = rf"""
\node[canvas is xy plane at z=0] ({name}) at {to}{{
\includegraphics[width={width}cm,height={height}cm]{{{pathfile}}}
}};
"""... | 61906516d85ba3311506067ad4ec81b32786f1b9 | 3,599,039 |
def CalculateMSE(xs_, mu):
"""
计算均方误差
xs_: 所有样本均值
mu: 总体均值
"""
return sum([(x - mu) ** 2 for x in xs_]) / len(xs_) | 45e90b003e05d54fdfa13c82b1f1332d0e68082a | 3,599,040 |
def load_hw10_starfit():
"""
Set up data for fitting
"""
hw10 = rd.load_hw10(as_number=True)
columns_to_use = np.array([isinstance(col, (int, np.integer)) for col in hw10.columns])
hw10logN = np.log10(hw10[hw10.columns[columns_to_use]])
return hw10, hw10logN | ff7387a8d1030f477469b7f335fa8d8ef9a07786 | 3,599,041 |
def is_init_authorised(context, bushfire_id):
"""
Usage::
{% if is_init_authorised %}
...
{% endif %}
or
{% for bushfire in object_list %}
{% is_init_authorised bushfire.id as init_authorised %}
<tr>
<td>{{ bushfire.id }}</td>
... | 377a4bfeffd8dc04751ad6264b3348845f638e69 | 3,599,042 |
def is_email(email: str):
"""Returns wether the given string is an email address or not"""
return EMAIL_REGEX.fullmatch(str(email)) is not None | a0a90a5a04f065058cee18f9479553aca2eec2e7 | 3,599,043 |
def gaussian_noise(images, bounds):
"""
adding gaussian noise to images. Different noise for each image.
:param images: RGB matrix of two input images. Nois on mask is useless
:param bounds: min and max of the gaussian noise
:return: RGB matrix with gaussian Noise
"""
noisy_images = images.c... | 8c6128534caa7ebaa8f817d0b8c70e72be19c2cd | 3,599,044 |
def voronoi(frame, data, f, parameters=None, call_num=None):
"""
Voronoi draws the voronoi network that surrounds each particle
Notes
-----
The voronoi cells must previously have been calculated in postprocessing.
See "voronoi" in postprocessing.
Parameters
----------
cmap_t... | 85cf9db42a82de3abae48131fb9df41151dde40b | 3,599,045 |
def _get_primary_status(row):
"""Get package primary status."""
try:
return row.find('div', {'class': 'pack_h3'}).string
except AttributeError:
return None | 1df5b66e9e16e17b8c5f3fc019a587a4320cf6d0 | 3,599,047 |
def calc_n_g(df_schedule, week):
"""Calculate list of games i.e. Ng
Each row has home_id, away_id, home_total_points, away_total_points
:param df_schedule: data frame with each matchup
:param week: current matchup period id
:return: list of games with team ids and scores for home/away
"""
df_scores = (
... | 1029840d28617d5de60fa499e8e6a9ae40b4ddea | 3,599,048 |
def is_string(s):
"""
Portable function to answer whether a variable is a string.
Parameters
----------
s : object
An object that is potentially a string
Returns
-------
isstring : bool
A boolean decision on whether ``s`` is a string or not
"""
return isinstance... | ed59a1b3a80f6695c971c49ff3b7936aa048523f | 3,599,049 |
def flatten_nested_list(list_in, drop_dupes=False):
"""Function to take a nested list and flatten into a 1d list."""
is_nested=True if type(list_in[0]) ==type(['v','d']) else False
if is_nested is True:
list_out=[];
if drop_dupes is True:
for list_i in list_in:
... | d3580ea22458ef794bdef280cec650f1521fda04 | 3,599,051 |
def computeIchimokuCloud(candleSet):
""" compute current values of Ichimoku Cloud lines """
conversionLine = (max([x["High"] for i,x in candleSet.iloc[-9:].iterrows()]) + min([y["Low"] for i,y in candleSet.iloc[-9:].iterrows()]))/2
baseLine = (max([x["High"] for i,x in candleSet.iloc[-26:].iterrows(... | 781df8f002a7fc3f5f7b3dd854a123bb3f21c142 | 3,599,052 |
def update_subfield_snippet(context,
code,
subfield,
value):
"""
Replaces a new value in a subfield snippet for a MARC field
:param context: Context
:param code: Field code
:param subfield: MARC subfield, if numeric... | 67ddf74faef5295028d27725534e05c888fba36a | 3,599,053 |
from typing import Sequence
from typing import Optional
def setup_minimal_example(
num_companies: int = _num_companies,
num_markets: int = _num_markets,
market_company_connections: np.ndarray = _market_company_connections,
market_capacities: np.ndarray = _market_capacities,
step_sizes: Sequence[fl... | 712f5b811767e37979670be001f4caf39aa18d04 | 3,599,054 |
import json
def follow(request, username,
template_name='relationships/relationship_add_confirm.html',
success_template_name='relationships/relationship_add_success.html',
mimetype='text/html'):
"""
Allows a user to follow another user.
Templates: ``relationships... | a2baf9c1aebdda46c4879db66d3e269d13fa3c18 | 3,599,055 |
from typing import Any
from typing import Optional
def _strat_has_unitary_from_has_unitary(val: Any) -> Optional[bool]:
"""Attempts to infer a value's unitary-ness via its _has_unitary_ method."""
if hasattr(val, '_has_unitary_'):
result = val._has_unitary_()
if result is NotImplemented:
... | 96965cfec5f5c3c29ea641c95f20a2d4b1f3b859 | 3,599,056 |
def write_keyvalue_toxlsx(worksheet, row, key, value):
"""Write a dictionary to excel sheet."""
if type(value).__name__ == 'dict':
worksheet.write(row, 0, key)
row += 1
for dictkey, dictvalue in value.items():
row = write_keyvalue_toxlsx(worksheet, row, dictkey, dictvalue)
... | c89111a35b182824664624c9b41358a15351f91d | 3,599,058 |
def swap_index_values(series: pd.Series) -> pd.Series:
"""Swap index and values in series.
Examples:
>>> s = pd.Series({'a': 1, 'b': 2})
>>> s.name = 'val'
>>> s.index.name = 'char'
>>> swap_index_values(s)
val
1 a
2 b
Name: char, dtype: obj... | 3849fd2c21bdd0782391f0030947fdcf3fca2251 | 3,599,059 |
def BFS(map_dict, start, end, verbose):
"""
BFS algorithm to find the shortest path between the start and end points
on the map, following the map given by map_dict
If an invalid end location is given then it will return the longest possible
path and its length.
Returns the length of t... | f6770fe8cd5f6c4ebffce2a78992c76cfbb5bd31 | 3,599,060 |
from config import get_config
import socket
def get_hostname(config=None):
"""
Get the canonical host name this agent should identify as. This is
the authoritative source of the host name for the agent.
Tries, in order:
* agent config (config.cfg, "hostname:")
* 'hostname -f' (on unix)
... | 5b5eff2b65b382689586e0ed6b5e71594054f9e9 | 3,599,061 |
def ir_swap_price(
pay_leg_coupon_rates,
receive_leg_coupon_rates,
pay_leg_notional,
receive_leg_notional,
pay_leg_daycount_fractions,
receive_leg_daycount_fractions,
pay_leg_discount_factors,
receive_leg_discount_factors,
dtype=None,
name=None):
"""Computes prices of a batch o... | 7c19f71c061992f17bf344ed69eaa45e01a81623 | 3,599,063 |
def construct_timeseries_cds(report):
"""Construct two standardized Bokeh CDS for the timeseries and scatter
plot functions. One with timeseries data for all observations,
aggregates, and forecasts in the report, and the other with
associated metadata sharing a common `pair_index` key.
Parameters
... | adeb72708a6c68c8f97a5e9006c5ff9f97029b58 | 3,599,064 |
from functools import reduce
def pipeline(source, functions):
"""Apply an array of functions to a source iterable"""
return reduce(lambda x, y: y(x), functions, source) | c79977ce46a6be15fc428530d94f8c4878a68ede | 3,599,065 |
def compare_proj4(original, new):
"""compare proj4 strings"""
def to_dict(proj4_str):
"""convert to dictionary"""
proj4_split = proj4_str.strip().split(" ")
proj4_dict = dict()
for elem in proj4_split:
elem_split = elem.strip().split("=")
try:
... | c7fba2e9f9257414e681d5aa28d55bada02ef61e | 3,599,066 |
import requests
def cube_instance(connection, cube_id, body={}, offset=0, limit=1000, verbose=False):
"""
Create a new instance of a specific cube. This in-memory instance can be used by other requests.
Args:
connection: MicroStrategy REST API connection object.
cube_id (str): Unique ID o... | 512962af29851c87bd278088aa69d0b9db967707 | 3,599,067 |
from pathlib import Path
def read_commit(wkspc_dir) -> str:
"""
read and return the value
in the commit file
:param wkspc_dir: jenkins workspace dicrectory
:return: commit tag value
"""
commit = 'false'
# confirm branch directory exists
vol_dir = '/'.join(['/volumes', PurePath(wk... | f547fd7619241549060f0156012e5ca89ceb42e0 | 3,599,069 |
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