sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
f789597d5ace0d07506a2f9c3ebca1f25881214bfbcb1f031a34d6a668e55dfe | Python | 831 | 20 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""check_us.py —— 扫描 main.tex 中 \texttt 与数学模式之外的裸下划线。"""
import re
src = open('game/docs/arxiv/main.tex', encoding='utf-8').read()
# 先抠掉 \texttt{...}（允许一层花括号嵌套）
stripped = re.sub(r'\\texttt\{(?:[^{}]|\{[^{}]*\})*\}', '', src)
bad = []
for i, l in enumerate(stripped.split(... |
8c57efbebecd713cc2bb3c2790bc413e9775c71582cf4d0b0f17c2763e42f9c1 | Python | 833 | 25 | import os
import sys
from pysitk.definitions import DIR_TMP
from pysitk.definitions import ITKSNAP_EXE
from pysitk.definitions import FSLVIEW_EXE
from pysitk.definitions import NIFTYVIEW_EXE
DIR_ROOT = os.path.realpath(
os.path.join(os.path.dirname(os.path.abspath(__file__)), ".."))
DIR_TEST = os.path.join(DIR_RO... |
c5d622f370ec468dba11305cdb93c604df4e8b404a790ac5fe47efea1d7e9508 | Python | 833 | 39 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
import common_setup
from relativescatter import RelativeScatterPlotReport
REVS = ["issue67-v4-base", "issue67-v4"]
SUITE = suites.suite_optimal_with_ipc11()
CONFIGS = {
"astar_blind": [
"--search",
"astar(blind())"],
... |
cf0d8ac4f41dddb246e4baaf1d653c7cb30ee2860f4e4247b10da58c3379de70 | Python | 833 | 25 | #from setuptools import setup, find_packages
from setuptools import setup
setup(
name='odopy',
version='0.1.0',
author='Brian Szekely and Christian Sinnott',
author_email='bszekely@nevada.unr.edu',
packages=['odopy', 'odopy.test'],
scripts=['src/odopy/headCalibrate.py'],
#url='http://pypi.python.o... |
0a6637a3e815fe3568afafa021d3d5f6170c8adf5e51f0f78d6e7883fbece677 | Python | 835 | 23 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
0a94385f5975769279b64e5f5aa120c2540efd645c47490ffd50feee9e0428ef | Python | 835 | 25 | #!/bin/env python3
"""
Ithildin module for Python
==========================
This module offers tools to read, analyse and draw the simulation results of
the C++ computational cardiology framework ithildin.
"""
from . import defect, filament, graph, log, pdl, phase, plot, pulse, points, simdata, topology, varfile, xd... |
1e31b821b86e0818cd3b526b75bb2de61a75bd19b71c73d41a5058c669a60dd0 | Python | 838 | 34 | from abc import abstractmethod
from collections.abc import Collection, Hashable, Iterator, Mapping
from typing import Protocol, Self
class ReadOnlyGraph[T: Hashable](Protocol):
@property
@abstractmethod
def nodes(self) -> Collection[T]: ...
@abstractmethod
def iter_edges(self) -> Iterator[tuple[T... |
b4a1669aea36c43f3498ab2d1b5907cdcfa416e6625586c0bf7b5f5434f2ce03 | Python | 838 | 24 | """
DeepLabCut2.0 Toolbox (deeplabcut.org)
copyright A. & M. Mathis Labs
https://github.com/AlexEMG/DeepLabCut
Please see AUTHORS for contributors.
https://github.com/AlexEMG/DeepLabCut/blob/master/AUTHORS
Licensed under GNU Lesser General Public License v3.0
"""
from deeplabcut.gui.analyze_videos import *
from deep... |
a1b895343bfe9f06905d5cae00acf95a2132b971669f7fbe668e92aff04ea187 | Python | 839 | 18 |
from .env_modules.createc_control import Createc_Controller
from .env_modules.episode_memory import Episode_Memory
from .env_modules.dissociate_env import DissociateEnv
from .env_modules.img_attrib import mol_property
from .env_modules.net_framework import img_classifier
from .env_modules.img_conv import ResNet1... |
c90d850d7f37bea00b1301556ca0c162f7b1935857a49d3dd22c0e735f5991e4 | Python | 839 | 27 | """Constants used the project. Mostly paths to data, logs and so on"""
import datetime
import os
import platform
from path import Path
PROJECT_ROOT = Path(os.path.dirname(os.path.dirname(__file__)))
ASSETS_ROOT = PROJECT_ROOT.joinpath("assets")
WEIGHTS_ROOT = ASSETS_ROOT.joinpath("model_weights")
LOGS_ROOT = ASSETS_R... |
ceee36ad7cefe59a684ce168dcf1144e8c3f9a11c099d1a37d8e24dc2448e0fe | Python | 839 | 30 | from experiment_management import run_experiment
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-d", action="store_true", help="Dry run")
parser.add_argument("-t", action="store_true", help="Test")
args = parser.parse_args()
if __name__ == '__main__':
experiments = ["BOTHCNN"]
dry_r... |
0c5e5126dc990807e87e4d4c73697be3f450fba09db5b8d7da33bb39ccb50abc | Python | 840 | 35 | from numpy import std, square, mean
def unsigned_to_signed(unsigned_array,N):
signed = 0
for i in range(N):
signed = signed + int(unsigned_array[i])*(256**(N-1-i))
signed = signed - (256**N)/2
return signed
def split_int_2byte(number):
return int(number)% 256,int(number) >> 8
def split_s... |
53f0f591baf734dc88c7fd03e5b1e67cd79814245bf42655a62fb14a18e82335 | Python | 840 | 34 | import asyncio
import inspect
import sys
from memory_profiler import profile
@profile
async def my_func():
a = [1] * (10 ** 6)
b = [2] * (2 * 10 ** 7)
await asyncio.sleep(1e-2)
del b
return len(a)
async def main():
# Ensure @profile preserves the coroutine nature of async functions so
#... |
61abbbee7045d36256a8a4eabab01bd8f5a4b525c927240365f6e21bc8f412d2 | Python | 840 | 15 | """
toposampling - Topology-assisted sampling and analysis of activity data
Copyright (C) 2020 Blue Brain Project / EPFL & University of Aberdeen
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation... |
46ede95bc17009c2836ae2c2db997330c09e72b570c633b2dcab89a1745fc37d | Python | 841 | 35 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
from lab.reports import Attribute
from lab.suites import suite_all
import common_setup
import os
exp = common_setup.IssueExperiment(
search_revisions=["issue469-base", "issue469-v1"],
configs={"astar_blind": ["--search", "astar(blin... |
8bb2a3de7cfa046fc012f4b0975df4b68d70a96dca23a08a28dd7d6cc42f72f6 | Python | 841 | 27 | import unittest
from file_io import load_image, save_image
from cythonized.ada_thr import adaptive_threshold
from cythonized.img_pca_filter import img_pca_filter
import time
class MyTestCase(unittest.TestCase):
def test_pca(self):
img = load_image(r"D:\GitHub\Segment-Splicing-Search\data\gt\512_128_tiff\1... |
96cb416a24ff395ab5162e13ea08f28aa8fa1a71d9348a8102a6bf21a429a4c9 | Python | 841 | 39 | from memory_profiler import profile
import logging
# create logger
logger = logging.getLogger('memory_profile_log')
logger.setLevel(logging.DEBUG)
# create file handler which logs even debug messages
fh = logging.FileHandler("memory_profile.log")
fh.setLevel(logging.DEBUG)
# create formatter
formatter = logging.Form... |
7beac81a672afd08fac0a67e4530a6972c69e11b7368934e50f88363cdcd4d0b | Python | 842 | 29 | import zipfile
import gdown
import os
import tarfile
import requests, io
def main():
## Download early model
r = requests.get("https://blastospim.flatironinstitute.org/html/early_embryo_model.zip")
z = zipfile.ZipFile(io.BytesIO(r.content))
z.extractall('.')
## Download late model
r = requests... |
a31a75440eb87b6ca4a28e7871f27bfa0f3535a5d0c9c24b913c5d6d2c482f28 | Python | 842 | 27 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
f3dd0ea6f9eb5816eca52aa8d7d1aa14d74dca7cccf6a60e9d3639d6ec67d74c | Python | 842 | 37 | # .. an example to test decorator with precision keyword argument ..
"""
Test profiling using imported ``profile`` function with precision
specified as parameter to ``profile`` function.
This is useful if the python script we're trying to profile is called as a
module (like `python -m test_module`) and so we can't spec... |
3f117ae47c842adcb486cb2031e145b6719fb3f6fdac435a0c1e0d6ba6113de6 | Python | 843 | 29 | #!/usr/bin/env python
from setuptools import setup
import progress
setup(
name='progress',
version=progress.__version__,
description='Easy to use progress bars',
long_description=open('README.rst').read(),
author='Giorgos Verigakis',
author_email='verigak@gmail.com',
url='http://github.c... |
e34db7e8f18da648305260d4193b8a95826e02fc70482d990b1c48ed64cc7cd0 | Python | 843 | 31 | # built-in
from math import isclose
# external
import pytest
# project
import textdistance
ALG = textdistance.JaroWinkler
@pytest.mark.parametrize('left, right, expected', [
('hello', 'haloa', 0.7333333333333334),
('fly', 'ant', 0.0),
('frog', 'fog', 0.9166666666666666),
('ATCG', 'TAGC', 0.8333333... |
f756b6994a3bbdbc9f65d70a636f2080c0b7a722f3b78ebcde92844c20a85e03 | Python | 843 | 53 | import os
import numpy as np
from PIL import Image
from topaz.utils.data.sampler import (RandomImageTransforms, ShuffledSampler,
StratifiedCoordinateSampler,
enumerate_coordinates)
class TestRandomImageTransforms():
def test_init(self):
... |
5a2b874c6e8ad0466f2c8d63c852b05c4aebb599247932566532be4f75078984 | Python | 847 | 31 | #!/usr/bin/env python2.7
from __future__ import print_function
import os
from pddlstream.algorithms.search import solve_from_pddl
from pddlstream.utils import read
from pddlstream.language.constants import get_length
def read_pddl(filename):
directory = os.path.dirname(os.path.abspath(__file__))
return rea... |
2056ee38e05d771d319fdce991300ef43aac23d58916b2706cd0d4e5d83a2a79 | Python | 848 | 29 | #!/usr/bin/env python
from setuptools import find_packages, setup
VERSION = "0.6"
with open("README.md", encoding="UTF-8") as f:
readme = f.read()
with open("requirements.txt", encoding="UTF-8") as f:
required = f.read().splitlines()
setup(
name="nucleus",
version=VERSION,
description="Nucleus ... |
39c9f622db02c7e39ae81a31ba5b60fbf6b98c79cb730a4c90da78c8e6de725c | Python | 848 | 26 | from google.cloud import pubsub_v1
import preprocessing
import json
import sys
def receive_messages(project, subscription_name):
"""Receives messages from a pull subscription."""
subscriber = pubsub_v1.SubscriberClient()
subscription_path = subscriber.subscription_path(
project, subscription_name)
... |
4d5aece563ee81d2328d145eb425ca0a165bf32248a5a9ab584192de9f11c0cc | Python | 848 | 32 | import sys
import matplotlib.pyplot as plt
angle = []
pdf = []
cdf = []
random_values = []
for line in open(sys.argv[1]) :
if line[0] == '#' : continue
values = line.strip().split()
if len(values) == 1 : random_values.append( float(values[0]) )
else :
angle.append( float(values[0]) )
pdf.append( float(... |
826a18518e988e198def63d6064cce59fa0207245c538a154f12daeba4e99a63 | Python | 848 | 30 | import torch
import torch.nn as nn
class RadialFunction(nn.Module):
"""
Contruct a radial function (linear layers + layer normalization + SiLU) given a list of channels
"""
def __init__(self, channels_list):
super().__init__()
modules = []
input_channels = channels... |
ddb7d3c8f0164f9184c5c3bc07e82fdd06277f9318823bf751545c3dfb610f23 | Python | 848 | 31 | import argparse
from pathlib import Path
import cv2
from tqdm import tqdm
def processing(img):
img = cv2.resize(img, (720, 720))
img = cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 21)
return img
def main(args: argparse.Namespace):
src = Path(args.source)
tar = Path(args.target)
pre... |
28940dd5e401afc8882b948aac9e3b957bf11b4049ecb9b7f16e334f4bfff259 | Python | 849 | 44 | from .package_data import __version__
from .core import (
IDNABidiError,
IDNAError,
InvalidCodepoint,
InvalidCodepointContext,
alabel,
check_bidi,
check_hyphen_ok,
check_initial_combiner,
check_label,
check_nfc,
decode,
encode,
ulabel,
uts46_remap,
valid_conte... |
62de24340774a43caffcd70384eee42ceb8dab59169ede4832fdf57e1cc9a7b0 | Python | 849 | 34 | import subprocess
def test_cli(tmp_path, stat_img):
output_dir = tmp_path / "mni_test"
output_dir.mkdir()
ret = subprocess.run(
[
"atlasreader",
"--atlas",
"harvard_oxford",
"aal",
"--threshold",
"7.0",
"--probabi... |
be1b6067619149d17a18eec3ccfb6b630388aff9bead5bc11e071a59ab0e18a5 | Python | 849 | 33 | from ..neuralnodes import params
import dataclasses
@dataclasses.dataclass
class Params(params.Params):
filter_inh_dur: float = 8.0
filter_inh_gain: float = 1.0
filter_delay: float = 7.0
filter_exc_dur: float = 4.0
filter_exc_gain: float = 0.1
ln2_filter_exc_dur: float = 1
ln2_filter_exc_g... |
d0c61d13ed61a7fd4443a73a2eb0eec50294d06d93b03bd3ae18f22f35bbe78f | Python | 849 | 30 | """
Helper functions
"""
import os
import pandas as pd
import warnings
def write_excel(save_to, dataframes):
"""
Write dictionary of dataframes to excel
Arguments:
save_to (str): path where to save
dataframes (dict): dictionary of dataframes
Returns:
None
"""
if os.path.... |
15f1e345ee5bb76b1f578b4d69e929550518742695aa7758bad9837fdd6f6c26 | Python | 850 | 21 | import numpy as np
from specs import *
class VoltageToFluorescenceConverter:
"""Logistic conversion."""
def __init__(self, specs: VoltageToFluorescenceSpecs):
a1 = np.exp(- specs.beta * specs.v1)
a2 = np.exp(- specs.beta * specs.v2)
z = (specs.f2 - specs.f1) / (specs.f1 * a1 - specs.f2... |
3eeba27e1462e52f60ac5f5ec3981e678504d93f621f7d2c79b9335c656c6135 | Python | 850 | 29 | import pytest
import elasticai.creator.nn.fixed_point as nn_creator
from elasticai.creator.arithmetic import FxpArithmetic, FxpParams
from tests.integration_tests.nn.fixed_point.precomputed_routine import (
routine_testing_precomputed_module,
)
@pytest.mark.ghdl
@pytest.mark.parametrize(
"total_bits, frac_bi... |
1f9130d997129c8ff10f818d0ae248ac5db39d028bfb91c844f0a7cb5d6b4209 | Python | 851 | 26 | from OnlineDetectors.online_nab_detector import OnlineAnomalyDetector
from OnlineDetectors.contextOSE.cad_ose import ContextualAnomalyDetectorOSE
class ContextOSEDetector(OnlineAnomalyDetector):
"""
This detector uses Contextual Anomaly Detector - Open Source Edition
2016, Mikhail Smirnov smirmik@gmail.... |
ac12d2c14efa88f68ac2cb1e1b3381e69514cf07cc9ace3e1b171c4267638c52 | Python | 851 | 23 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
c348caa0a3531d643c43f59659ba32cbfd475aabe2f2ecedbf9ec87115ba1250 | Python | 851 | 36 | import torch
import torch.nn as nn
class Autoencoder(nn.Module):
def __init__(self, input_dim, d):
super().__init__()
self.d = d
self.encoder = nn.Sequential(
nn.Linear(input_dim, 1028),
nn.ReLU(),
nn.Linear(1028, 512),
nn.ReLU(),
... |
1cf05b29848345c291b6d19d48d2a02504a3ecb812b2f6ecba9add0c2ff3ca60 | Python | 852 | 26 | import numpy as np
from nodebpy.nodes.geometry import CurveToMesh, SetHandleType, SetSplineType
from molecularnodes.nodes.geometry import AtomsToCACurves
def test_atoms_to_ca_splines(fetch):
"""
This test is for if the chains are still connected as continuous, even though they are
separate by a larger dis... |
577c5f0698de4420bbe1776c1a39dda922bc2f12f7ace193c3786e9eb45771e3 | Python | 852 | 26 | #
# Copyright Bradley Lowekamp
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0.txt
#
# Unless required by applicable law or agreed ... |
5a0ada71e5f77474963efcaa11175d9bc6bd809e1b0af20531fa8b8b825adf8e | Python | 852 | 30 | # -*- coding: utf-8 -*-
from glob import glob
import os
import pytest
from dcm2bids.sidecar import Sidecar
@pytest.fixture
def sidecarFiles():
dataDir = os.path.join(os.path.dirname(__file__), "data", "sidecars")
return glob(os.path.join(dataDir, "*.json"))
def test_sidecar_lt(sidecarFiles):
sidecars... |
b000469d851a528095da3647d8aa336a4ff1c62ad4af0186ea3e1b9fa64442a0 | Python | 852 | 32 | #54121 -+sycu 6y
from psychopy import parallel
import serial
##### Set port address to match your local machine
paddress = '/dev/parport0' # '0xDFF8' or 'COM1' in Windows
##################################################
try:
port = serial.Serial(paddress)
port_type = 'serial'
except NotImplementedError:
... |
f35a545859bb9453c570a25450017e4585b2db452267a5588d4b5ee0b03475ca | Python | 852 | 16 | # this file must be run if changes are made to the model.py file
from numba.pycc import CC
from ednm.model import Model
cc = CC('aot_jacobian')
@cc.export('jacobian', '(float64, int32, float64, int32, int32, float64[:,:], float64[:,:], float64[:,:,:], float64[:,:,:], float64[:, :], float64[:, :], \
float... |
ffd7a891a264b9bdfa15450c4fa3abbb1a8af02d179df2d837aaf01322b8932b | Python | 852 | 30 | #!/usr/bin/env python
#================================================================
# Copyright (C) 2022 Yufeng Liu (Braintell, Southeast University). All rights reserved.
#
# Filename : morphology_utils.py
# Author : Yufeng Liu
# Date : 2022-04-18
# Description :
#
#==============... |
6677e2765fa0ac209dd347f6536ba374036782d6c680d5d36ddd86f5c9a08581 | Python | 853 | 36 | #!/bin/env python3
'''
Draw Feynman diagrams for the quasi-particles in the 2D patterns of cardiac
arrhythmia
'''
import matplotlib.pyplot as plt
import sys
import yaml
from pdlfeynman.diagram import *
plt.rc('font', family='sans-serif')
plt.rc('text', usetex=True)
plt.rc('text.latex', preamble="\n".join([
r'\u... |
df529a667ce4671706bd04acf473f5c1b8549a666d3c9de7038edaf06db9b3e9 | Python | 853 | 36 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
from lab.reports import Attribute, gm
import common_setup
def main(revisions=None):
SUITE = suites.suite_satisficing_with_ipc11()
CONFIGS = {
'cea': ['--search', 'eager_greedy(cea())'],
'cg': ['--search', 'eager_gree... |
1ad13fe058784241cc4e4db9e23214a558b965240cb9ea7be493f36ddb0a5248 | Python | 854 | 30 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author: Mitchell R. Vollger
import argparse
import pysam
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="", formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("infile", help="input fasta fi... |
8f209e85096ef2d9bcffd691b5d7b5a9a77edf67d90fc45415d371f645b23f26 | Python | 854 | 29 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
''' Submit to cluster ERF function '''
import os
import os.path as op
import numpy as np
import pickle
from warnings import filterwarnings
from sys import argv
from stormdb.cluster import ClusterBatch
from stormdb.access import Query
project = 'MINDLAB2020_MEG-AuditoryPat... |
82e970cb13eeb8f2e8460488c0b48870c7ebeb02528d16d5f2d354181b3134c5 | Python | 855 | 32 | #!/usr/bin/env python
# -*- encoding: utf-8 -*-
import sys
import os
sys.path.insert(1, os.path.join("../../../../h2o-py"))
import h2o
from tests import pyunit_utils
import requests
def redirect_relative():
conn = h2o.connection()
# get default requests arguments
req_args = conn._request_args()
heade... |
a8a1166ebf24408160f93455ec17840fe159a1c4a5486309390fe4f51138bd76 | Python | 855 | 33 | import os
import sys
sam_file_path = sys.argv[1]
readids_file_path = sys.argv[2]
test = sam_file_path.strip().split(".")
sam_file = open(sam_file_path)
seq_dict = {}
for line in sam_file:
if not line.startswith("@"):
line = line.strip()
words = line.split()
if(words[0] not in seq_dict.key... |
26f80cb4673bf65f3a2f031b538a8aed8e161ce03e3d248f627deb124a061b29 | Python | 856 | 29 | from typing import Iterator
from elasticai.creator.hdl_ir import Registry
def m_to_n_lut_cost(m: int, n: int) -> int:
return int(n / 3.0 * (2 ** (m - 4) - (-1) ** m))
def estimated_lower_lut_cost_by_implementation(
reg: Registry,
) -> Iterator[tuple[str, int]]:
for name, impl in reg.items():
if... |
a667df885a43dab9453593e6501fb11f3233fc2d1b3f67d21adc1d989cbd14a0 | Python | 856 | 33 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import downward.configs
import downward.suites
# "ipc=False" skips portfolio configurations which we don't need to
# test here.
CONFIGS = downward.configs.default_configs_optimal(ipc=False, extended=True)
# pathmax is gone in this branch, remove it:
for key, value in li... |
b94a3de2f0e3391c975dad6f7f4e3c3fa7304720e04b30d771e7986be9c7fcbc | Python | 856 | 27 | import numpy as np
from PIL import Image
from scipy.stats import pearsonr
def load_images(image_paths):
images = []
for path in image_paths:
img = Image.open(path).convert('L')
img_array = np.array(img)
images.append(img_array)
return images
def calculate_plcc(subjectiv... |
c5603f8a9e5163cef22bc2357ecc8b8dd19554229f43bc7aaa104eebc9393629 | Python | 856 | 28 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
import sys
from cirrocumulus import concat, launch, prepare_data, serve
def main():
command_list = [concat, launch, prepare_data, serve]
parser = argparse.ArgumentParser(description="Run a cirro command")
command_list_strings = list(
m... |
422717b3c4acd70ddc8d1a2123f806dac93182d9d7d4c43adc7bff4036cf763e | Python | 857 | 17 | """Merge the part files of REAL_ODOURS into 06-OUTPUT/real_odours_counts.npz (row k kept from whichever file ran it). 2026-09-25."""
import numpy as np, glob, os
OUT = 'D:/eucys/06-OUTPUT/real_odours_counts.npz'
Z = dict(np.load(OUT))
for f in ['D:/eucys/tmp/real_odours_counts_B.npz'] + sorted(glob.glob('D:/eucys/tmp/r... |
db7e766a027fa7ed79a1007cad7cf0e74b6d03fe54411e5bebaf3aeeba6e938d | Python | 858 | 34 | import unittest
import torch
from torcheeg.models import GRU, LSTM
class TestRNN(unittest.TestCase):
def test_gru(self):
model = GRU(num_electrodes=32, hid_channels=64, num_classes=2)
eeg = torch.randn(2, 32, 128)
pred = model(eeg)
self.assertEqual(tuple(pred.shape), (2, 2))
... |
15ac70e4db55db61f01ebc8dc32f69033efa8c706042613220a67e0991fb09dd | Python | 859 | 34 | from collections import Mapping
#class StarRoadmap(Mapping, object):
class StarRoadmap(Mapping, object):
def __init__(self, center, planner):
self.center = center
self.planner = planner
self.roadmap = {}
"""
def __getitem__(self, q):
return self.roadmap[q]
def __len__... |
396c02a5a3172842e008c4e53a2a5a0ed9d7f0148b5ffbd6138b094e87896b16 | Python | 859 | 40 | ## moving point charge with superluminal phase velocity in dielectric media emitting Cherenkov radiation
import meep as mp
sx = 60
sy = 60
cell_size = mp.Vector3(sx, sy, 0)
dpml = 1.0
pml_layers = [mp.PML(thickness=dpml)]
v = 0.7 # velocity of point charge
symmetries = [mp.Mirror(direction=mp.Y)]
sim = mp.Simulat... |
52436c008eeb17dc078ddea2a3906b9a84aa9d67cd3539f5f15c44faf8b7ffbb | Python | 859 | 18 | import os, sys
BASE = os.path.dirname(os.path.abspath(__file__))
FILES_PATH = os.path.join(BASE, "files")
TRAINED_NN_PATH = os.path.join(FILES_PATH, "NN")
TRAINED_NN_MSEED = os.path.join(TRAINED_NN_PATH, "NN_MS.npz")
TRAINED_NN_BIGG = os.path.join(TRAINED_NN_PATH, "NN_BG.npz")
MODELS_PATH = os.path.join... |
a38453e3111a7f2d5ca23c8cf12f4265c6be8b76d9a5415320ceca4830a1608f | Python | 859 | 31 | import os
import random
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from config import *
from core.worker import main_worker
if __name__ == '__main__':
args = get_parser()
cudnn.benchmark = True
if args.debug:
rank = 0
local_rank = 0
else:
... |
eaff6ebc5a89d672e58e2998a397908f80dc3db6d1b075df5d3cf6b6b0b36b47 | Python | 859 | 24 | from regtools.registration.report import _render_grouped_metadata
def test_groups_known_keys():
meta = {
"atlas": "allen_ccf_25um",
"registration_method": "ants",
"random_seed": 42,
"laplacian": {"rtol": 0.01},
"timing": {"total_sec": 123.4},
"environment"... |
517c6270f2988ce83a7136d47ab4687bdf02366bd338c5c8fb55a2315d99c357 | Python | 860 | 17 | """
toposampling - Topology-assisted sampling and analysis of activity data
Copyright (C) 2020 Blue Brain Project / EPFL & University of Aberdeen
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation... |
86f0269a25a567f6f02601f5387cbaf1527cfa90ff368db36560ca8f53f3958c | Python | 860 | 19 |
df = pd.read_csv('Exp_Pl_protID_to_OGID.csv')
# Find entries present in all columns
conserved = set(df['ath']).intersection(df['zma'], df['ppp'], df['cre'])
# Find entries present only in each column
only_ath = set(df['ath']) - set(df['zma']) - set(df['ppp']) - set(df['cre'])
only_zma = set(df['zma']) - set(df['ath'])... |
9a1fda9291c95d3b2499febf9f86a2871b6036dc2eb63ba6e2af4387c1715968 | Python | 860 | 31 | # Configuration file for the Sphinx documentation builder.
import os
import sys
sys.path.insert(0, os.path.abspath(".."))
project = "ColabMDA"
copyright = "2024, Shammit Kumar Paul"
author = "Shammit Kumar Paul"
release = "0.1.0"
# -- General configuration ---------------------------------------------------
extensio... |
b6933140dd184239d6566b12c30f647784b320fd0c57303435256f3f12c9d54e | Python | 860 | 31 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
from common_setup import IssueConfig, IssueExperiment
configs = [
IssueConfig(
"cegar-10K-original",
["--search", "astar(cegar(subtasks=[original()],max_states=10000,max_time=infinity))"]),
IssueConfig(
"cegar... |
ce18295c233e11b8dc54a8548ccd25b23ef42c47b564b0af79fd8b9b7decdea5 | Python | 860 | 17 | """
toposampling - Topology-assisted sampling and analysis of activity data
Copyright (C) 2020 Blue Brain Project / EPFL & University of Aberdeen
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation... |
f77c3cd395c6af40e6d37498607d3fc6aef4c8e496b2ffc36320c9646ccfab02 | Python | 862 | 31 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
from common_setup import IssueConfig, IssueExperiment
configs = [
IssueConfig(
"cegar-10K-original",
["--search", "astar(cegar(subtasks=[original()],max_states=10000,max_time=infinity))"]),
IssueConfig(
"cegar... |
442924bbf07b8ae7c70891b741df68f942892e8bd3efdac1dc011e67ce6675ca | Python | 863 | 17 | try:
from setuptools import setup
except ImportError:
from distutils.core import setup
setup(
name='classification_models_3D',
version='1.1.0',
author='Roman Sol (ZFTurbo)',
packages=['classification_models_3D', 'classification_models_3D/models'],
url='https://github.com/ZFTurbo/classificat... |
522e0d764c56a2cfff7c6a4d22023f5114437c1a52409bd0a725befcafba51d1 | Python | 863 | 31 | import h5py
import numpy as np
colors = {0: 'CO', 1: 'C1',
2: 'C2', 3: 'C3',
4: 'C4', 5: 'C5',
6: 'C6', 7: 'C7',
8: 'C8', 9: 'C9',
10: 'C0'}
def load_data(h5_path, dataset_path, return_labels=False, remove_zero=True, shuffle=True):
with h5py.File(h5_path, 'r') as... |
599a6f39d873f6db536a4f0c0f7d53c6a6f0970c25f18263dc18db6b6ede1963 | Python | 863 | 33 | import sys
from pybioshell.std import vector_std_shared_ptr_core_data_sequence_Sequence_t
from pybioshell.core.data.io import read_clustalw_file
from pybioshell.core.data.sequence import SequenceProfile
if len(sys.argv) < 2 :
print("""
Reads a multiple sequence alignment (MSA) (in .aln format) produced by Clusta... |
5e436e62cd3ffc2f0ab9930a6ae722df42be0114e0d9ae573ba36dae565e1c56 | Python | 863 | 33 | import sys
from pybioshell.core.data.io import Cif, write_mol2
from pybioshell.core.chemical import MonomerStructure
if len(sys.argv) < 2 :
print("""
Converts a small molecule structure from CIF to MOL2 file format.
The last, optional parameter of the script provides the name of a given
molecule, that will be s... |
626ea282ec36cd9814aff721ccffa7bb3b2ae1e8a71b260325015e47da22e743 | Python | 863 | 23 | """Tests for loading nexus device configuration."""
from pathlib import Path
from pypulseq import Opts
from console.interfaces.device_configuration import NexusConfiguration, RxConfiguration, SystemLimits, TxConfiguration
from console.utilities.load_configuration import load_nexus_config, load_system_limits
def tes... |
efc264164b0051869b2e862ca46aad416d6eb09ba4369974e51ec21ed450b545 | Python | 863 | 33 | """
Contains Exception classes for image io.
# Author: Vladan Lucic Max Planck Institute for Biochemistry
# $Id$
"""
from builtins import str
__version__ = "$Revision$"
class FileTypeError(IOError):
"""
Exception reised when nonexistant file type is given.
"""
def __init__(self, requested, defined):... |
0c21b08169f89a31661a7418028be4ee165dc24c3c66deaeeac4d31729055416 | Python | 864 | 25 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
61de268be77a8ea98df7b789764437d349ea05ee60312c3aed39827d2ae7d5ec | Python | 864 | 29 | import numpy as np
import os
import merlin
from merlin.util import imagereader
from merlin.util import dataportal
root = os.path.join(os.path.dirname(merlin.__file__), '..', 'test')
def test_read_dax():
print(root)
dataPortal = dataportal.LocalDataPortal(
os.path.join(root, 'auxiliary_files'))
d... |
c4ba6ac2f8ccb0841937e05147f596fc1b75eb01b28c38d691285c264b8ac239 | Python | 864 | 36 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward.suites import suite_optimal_with_ipc11
from downward.configs import default_configs_optimal
from downward.reports.scatter import ScatterPlotReport
import common_setup
REVS = ["issue214-base", "issue214-v3"]
CONFIGS = {"ipdb": ["--search", "astar(ipdb())"]... |
36598b1c2f0ab5b62f1e8eb8abe7f9d512e1c459ef0d3001368d3568868f3d68 | Python | 865 | 27 | import sys
import os
data_file = open(sys.argv[1])
total_duration = 0
num_samples = 0
total_reads = 0
last_duration = -1
for line in data_file:
if " completed reads" in line:
total_reads += int(line.strip().split(":")[-1])
continue
elif "duration" in line:
secs = line.strip().split(":... |
425987db2c51a7d1ad594af587837fc15a50199f2b5faf7cd98e8db1a33b9779 | Python | 865 | 27 | import numpy as np
from PIL import Image
from scipy.stats import spearmanr
def load_images(image_paths):
images = []
for path in image_paths:
img = Image.open(path).convert('L')
img_array = np.array(img)
images.append(img_array)
return images
def calculate_srocc(subject... |
c7fa621812ed5d5256bbf012c95cdae497822c3fe26b0661f9a20c6dfcaa5043 | Python | 865 | 36 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward.suites import suite_optimal_with_ipc11
from downward.configs import default_configs_optimal
from downward.reports.scatter import ScatterPlotReport
import common_setup
REVS = ["issue214-base", "issue214-v4"]
CONFIGS = {"ipdb": ["--search", "astar(ipdb())"]... |
d53d55d91cc0355ce21ce3020ed6eaf5a2a6eaa48474aad5bdaf7c8d6904c49e | Python | 865 | 24 | import os
import glob
import csv
from nltools.data import Brain_Data
from nltools.mask import expand_mask
import pandas as pd
mask = Brain_Data('/home/intoumanis/sugar/code/other/AAL3v1.nii')
mask_x = expand_mask(mask)
# Read all the cropped nifti files, extract the avg BOLD within each ROI and save it in a csv file ... |
0b6d5f65a14e0f8c53f97e65dbf28c8a3ac8fb65f3d9feef5cf9d04d16ba20e0 | Python | 866 | 27 | import os
import numpy as np
from cmdstanpy import CmdStanModel
# example usage for single animal
data_path = "/jukebox/witten/yoel/saved_results"
mouse_id = 26
data = np.load(os.path.join(data_path, f"behavior_data_mouseid_{mouse_id}.npy"))
data_for_stan = {
"X": data["X"],
"y": data["y"].astype(int),
"... |
8e99afe2f0a6f55cac560142e8c057806c9f043bba95a8dd5a6f9ed18c5efdc0 | Python | 866 | 33 | import yaml
import sys
import argparse
import shutil
import os
import BEBE.evaluation.evaluation as evaluation
import BEBE.utils.experiment_setup as experiment_setup
import BEBE.training.train_model as train_model
def main(config):
expanded_config = experiment_setup.experiment_setup(config)
model = train_model.tr... |
68818cdd8907a8add31d2e065b7061ad89b932d9f20f4bb7f488e628865994ac | Python | 867 | 36 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward.suites import suite_optimal_with_ipc11
from downward.configs import default_configs_optimal
from downward.reports.scatter import ScatterPlotReport
import common_setup
REVS = ["issue214-base", "issue214-v5"]
CONFIGS = {"blind": ["--search", "astar(blind())... |
36cea6a8543cdbe71ecffa260c17f7cf656dc810ec50287ab5e413d3d0d91680 | Python | 868 | 27 | from collections import defaultdict
from pddlstream.utils import INF
class ActionInfo(object):
def __init__(self, terminal=False, p_success=None, overhead=None):
"""
:param terminal: Indicates the action may require replanning after use
"""
self.terminal = terminal # TODO: infer f... |
9feb9332d000a9a958b6957d0dfc66f4f9b2d10bb450c7979311595c3ac81d38 | Python | 868 | 34 | """Standalone GUI launcher for the Point Reorient tool."""
import sys
from regtools.gui.app_scaffold import AppScaffold
from regtools.point_reorient.gui import PointReorientTab
class PointReorientApp(AppScaffold):
def __init__(self):
super().__init__(
title="Xu Lab - Point Reorien... |
cb38a174763a2189ad7641c88fe71c617005f6a6238ef2e4086e6140868e0a5e | Python | 869 | 28 | from __future__ import annotations
from pathlib import Path
import pytest
from ase.io import read
from numpy.testing import assert_equal
from quacc.atoms.deformation import make_deformations_from_bulk
FILE_DIR = Path(__file__).parent
def test_make_deformations_from_bulk():
atoms = read(FILE_DIR / "ZnTe.cif.gz... |
6034ce6b1fe277bbb389ce0a4122af252a7858b54ba3de42594ff86c1e78dea3 | Python | 870 | 11 | #----------------------------------------------------------------------------------------------------------------------------
# Author: Brandon S Coventry CAP Lab Purdue University Weldon School of BME
# Date: 09/02/20
# Revision History: N/A
# Purpose: This is the setup file to get spiking reinforcement learni... |
e40814ddd864573b1e1d1612992cbc81bec040fb4b97a89668c56b4ec9c67cec | Python | 870 | 28 | import re
import os
from pyfaidx import Fasta
from otscore import calcCcTopScore
for file in os.listdir('../../OT/'):
tech = file.split('.')[0]
namelst = ['sgID', 'seq', 'mismatch', 'chr', 'strand', 'start', 'end', 'span', 'score']
f = open('OT/' + file, 'w')
f.write('{0}\n'.format('\t'.join(namelst)))
... |
240da6b43d5d1fe6e4a20647d9249a4660e345ecf72d75fd90dd4cf29484d953 | Python | 871 | 36 | import coverage
import pytest
import os
import sys
WARN_TEXT_COLOR = "\033[93m"
WARN_PERCENT = 60
cov = coverage.Coverage()
cov.start()
retcode = pytest.main(["tests/unit"])
passing = retcode == 0
if len(sys.argv) > 1 and sys.argv[1] == "i":
retcode = pytest.main(["tests/integration"])
passing = passing and... |
53f3a3cd98b4b84e1728d7b93d709220ef6a4eb850d44c3aa1aefdc954a2b07b | Python | 871 | 31 | """This module contains custom exceptions for Bayesian Optimization."""
from __future__ import annotations
__all__ = [
"BayesianOptimizationError",
"ConstraintNotSupportedError",
"NoValidPointRegisteredError",
"NotUniqueError",
"TargetSpaceEmptyError",
]
class BayesianOptimizationError(Exception... |
0aef456d042ded42fa84e731f444199ebc08e1e96cf147c121a05c49afada683 | Python | 872 | 32 | import os
import tempfile
import unittest
from torcheeg.model_selection import LeaveOneSubjectOut
from .test_utils import _make_dataset
class TestLeaveOneSubjectOut(unittest.TestCase):
def setUp(self):
self._directory = tempfile.TemporaryDirectory()
self.addCleanup(self._directory.cleanup)
... |
bea293c97a1baa9cc5eeedd6e25bc11c5f69a96ef82d97eac2a66ba85634bce4 | Python | 872 | 25 | import argparse
import numpy as np
import pandas as pd
from scipy import stats
# For whole data prediction
# loc : mean of standardized expression from train data
# scale : 1
def postprocess(x):
return stats.norm.cdf(x, loc=0.06201038511068657, scale=1)
if __name__ == '__main__':
parser = argparse.ArgumentP... |
69a71e8f7920bcd1eea3cd712cfb68bd6edc9fa449dcf0498cda228343b7f70e | Python | 873 | 28 | import re
import os
from pyfaidx import Fasta
from otscore import calcCropitScore
for file in os.listdir('../../OT/'):
tech = file.split('.')[0]
namelst = ['sgID', 'seq', 'mismatch', 'chr', 'strand', 'start', 'end', 'span', 'score']
f = open('OT/' + file, 'w')
f.write('{0}\n'.format('\t'.join(namelst)))... |
e19c5ef733dfaffb341e1fb272cf8eac865b3c070619ccca08e352d42bde82b7 | Python | 873 | 21 | # regtools/tests/test_ccf_session_section_index.py
import json
from regtools.ccf_boundary_editor.session import save_session, load_session
def test_section_index_round_trips(tmp_path):
p = tmp_path / "s.json"
save_session(
str(p), slice_states=[], input_paths={"annotation": "a.nii.gz"}, curren... |
2ebb7080751d1a79f4a2165358862459f2c31b7133dd629731cea3d1b2c08d45 | Python | 874 | 31 | """CLI flag parsing for the 2D atlas edit mask (--atlas-edit-mask-path / --atlas-edit-fill)."""
from regtools.registration.cli import build_parser
def test_cli_parses_atlas_edit_flags():
parser = build_parser()
args = parser.parse_args(
[
"--fixed_image",
"t.nii.gz",... |
2602cd230348a42d95e8af6aa019f3555190beea37906881d0498146fcddb9fd | Python | 875 | 29 | import os
os.environ["TORCH_HOME"] = "."
import torch
from PIL import Image
from torchvision import models
from cog import BaseRunner, Input, Path
WEIGHTS = models.ResNet50_Weights.IMAGENET1K_V1
class Runner(BaseRunner):
def setup(self) -> None:
self.device = torch.device("cuda" if torch.cuda.is_avail... |
4032c7822adf73aff88a377381df93eef3e96a537f40c49513427e4a28face99 | Python | 875 | 29 | import matplotlib.pyplot as plt
def set_style(style='default', w=1, h=1):
sdict = {
'default': {
'figure.figsize' : (4.98 * w, 2 * h),
'lines.linewidth': 1,
'lines.markersize': 1.5,
'font.size' : 10,
'legend.frameon' : True,
'font.family'... |
6350d4494234bf623f305fb2781bc995751b3aab9643d4697593e9ab34c39461 | Python | 877 | 29 | """Helpers for the optional JAX backend."""
from __future__ import annotations
from contextlib import contextmanager
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from collections.abc import Iterator
_JAX_STACK = frozenset({"jax", "jaxlib", "numpyro", "ott", "flax", "optax"})
@contextmanager
def jax_impo... |
52aff94865e4215b9d35286282754cb0413314524a753d1cb1dfa67e457ef0c8 | Python | 878 | 34 | import torch
from torch_geometric.data import InMemoryDataset
class GNNDataset(InMemoryDataset):
def __init__(self, root, train=True, transform=None, pre_transform=None, pre_filter=None):
super().__init__(root, transform, pre_transform, pre_filter)
if train:
self.data, self.slices = to... |
a842faf27e3799fe284891d7103deab2d8f53d9a0d1c0f2a6b4cae0d3725ec35 | Python | 878 | 27 | #******************************************
#
# SHARC Program Suite
#
# Copyright (c) 2023 University of Vienna
#
# This file is part of SHARC.
#
# SHARC is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software F... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.