sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
df96f2bcb802c422a4a82f62666f0e7e0472c2cf3ea4de816520947dceca17b7 | Python | 376 | 14 | #!/usr/bin/env python
import sys
from setuptools import setup
import versioneer
SETUP_REQUIRES = ['setuptools >= 30.3.0']
SETUP_REQUIRES += ['wheel'] if 'bdist_wheel' in sys.argv else []
if __name__ == "__main__":
setup(name='phys2bids',
setup_requires=SETUP_REQUIRES,
version=versioneer.get_v... |
0f87f1570ee2b3bce0ace9d535b30e11970b5554e059374e14c1c1fc03fd090f | Python | 377 | 15 |
from torch.utils.ffi import _wrap_function
from ._nms import lib as _lib, ffi as _ffi
__all__ = []
def _import_symbols(locals):
for symbol in dir(_lib):
fn = getattr(_lib, symbol)
if callable(fn):
locals[symbol] = _wrap_function(fn, _ffi)
else:
locals[symbol] = fn
... |
77dc92f3ecf2cad249c0f106fcb046acd2b4b54c7481fe4d8167634609cc720b | Python | 377 | 16 | import numpy
from Cython.Build import cythonize
from setuptools import Extension, setup
# Define Cython extensions
ext_modules = [
Extension(
"polychrom._polymer_math",
["polychrom/_polymer_math.pyx", "polychrom/__polymer_math.cpp"],
include_dirs=[numpy.get_include()],
)
]
setup(
e... |
b12616be01603e4452c272c6e169d836d6ea490dd38dbd44ecb7aecdcec7d6e2 | Python | 377 | 16 | """
https://stackoverflow.com/questions/40748687/python-api-rate-limiting-how-to-limit-api-calls-globally
"""
from ratelimit import limits, sleep_and_retry
# calls per minute
CALLS = 300
RATE_LIMIT = 60
#CALLS = 1
#RATE_LIMIT = 10
@sleep_and_retry
@limits(calls=CALLS, period=RATE_LIMIT)
def check_limit():
""" Emp... |
21b0d65aecd46f0cb710efc244dd8f6d7afd40f5ef0259f2f7e9391c01bb6be5 | Python | 378 | 17 | from dataclasses import dataclass
from ontoportal_client import EcoPortalClient
from oaklib.implementations.ontoportal.ontoportal_implementation_base import (
OntoPortalImplementationBase,
)
@dataclass
class EcoPortalImplementation(OntoPortalImplementationBase):
"""
Implementation over ecoportal endpoin... |
c42232cb0427418a5c5d1fe39896cd2a5b870c7d61475da66b76bc978a8f9b91 | Python | 378 | 14 | """
© 2022, ETH Zurich
"""
from rescoss_logp_ml.utils import PROC_DATA_PATH, FOLDER_NAMES
import pickle
import os
def load_dataset(dataset, feature_type):
savepath = os.path.join(PROC_DATA_PATH, FOLDER_NAMES[dataset], f"{dataset}_{feature_type}.pkl",)
with open(savepath, "rb") as handle:
df, features_... |
caf18dd9a8e0d4e8afeeb51a029b176ac39d0d399fc6ac24c0516df29106e607 | Python | 378 | 17 | from dataclasses import dataclass
from ontoportal_client import MatPortalClient
from oaklib.implementations.ontoportal.ontoportal_implementation_base import (
OntoPortalImplementationBase,
)
@dataclass
class MatPortalImplementation(OntoPortalImplementationBase):
"""
Implementation over matportal endpoin... |
38b0a6c38cd1eb5e0ee4e3340c449525f2067c1eefea92ea28474f5283610db5 | Python | 379 | 10 | from .base_bbox_coder import BaseBBoxCoder
from .delta_xywh_bbox_coder import DeltaXYWHBBoxCoder
from .legacy_delta_xywh_bbox_coder import LegacyDeltaXYWHBBoxCoder
from .pseudo_bbox_coder import PseudoBBoxCoder
from .tblr_bbox_coder import TBLRBBoxCoder
__all__ = [
'BaseBBoxCoder', 'PseudoBBoxCoder', 'DeltaXYWHBBo... |
65d3568b8df58cb044402a7461216f4bd564884c09ff7e938fd6c14a00a98d5e | Python | 379 | 7 | from .er import erdos_renyi_test, erdos_renyi_test_paired
from .fisher_exact_nonunity import fisher_exact_nonunity
from .sbm import fit_sbm, stochastic_block_test, stochastic_block_test_paired
from .degree import degree_test
from .binomial import binom_2samp, binom_2samp_paired
from .combine import combine_pvalues
from... |
1ac85d818d646d122f7888a3b596cab9e582c1b19ac3c65c3180719a6c02f233 | Python | 380 | 17 | from setuptools import setup
setup(
name='dni-pytorch',
version='0.1.0',
author='Piotr Kozakowski',
author_email='kozak000@gmail.com',
url='https://github.com/koz4k/dni-pytorch',
description=(
'Decoupled Neural Interfaces using Synthetic Gradients for PyTorch'
),
py_modules=['d... |
560dbe92721fb034a9511233730e8b09f8cdd8aa6587dce931f18a4f833cfa1e | Python | 380 | 10 | try:
from .fused_act import FusedLeakyReLU, fused_leaky_relu
from .upfirdn2d import upfirdn2d
# print('Using custom CUDA kernels')
except Exception as e:
# print(str(e))
# print('There was something wrong with the CUDA kernels')
# print('Reverting to native PyTorch implementation')
from .na... |
b29f1b6d87131917999ba1e7fde15a725a0a61c30205e4ee4ef7e67fe9b96c2a | Python | 380 | 18 | """
https://stackoverflow.com/questions/40748687/python-api-rate-limiting-how-to-limit-api-calls-globally
"""
from ratelimit import limits, sleep_and_retry
# calls per minute
CALLS = 300
RATE_LIMIT = 60
# CALLS = 1
# RATE_LIMIT = 10
@sleep_and_retry
@limits(calls=CALLS, period=RATE_LIMIT)
def check_limit():
"""... |
ea643edd9b5b7775494461837031ad420aaeba8e380610ef53809da51e3c142b | Python | 380 | 19 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Main script to run the AxoID GUI.
Created on Wed Jun 19 11:39:58 2019
@author: nicolas
"""
import os.path
import sys
# Add parent folder to path in order to access `axoid`
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from axoid.... |
55a2a1b502a1dab6c180dbf636c81640fc8cdbf240c245d667b021e291a0ac3e | Python | 381 | 7 | #### MODIFY THESE 2 LINES ####
motion_compensation_path = "/home/user/talabot/motion_compensation/run_motion_compensation.sh" # path to run_motion_computation.sh
matlab_path = "/usr/local/MATLAB/R2019a" # path to a MATLAB release
#############################
# Command imported by run_axoid.py script. Do not modify.... |
7a26af14cf1b955c193af1827866b2e1d003cf4dd7ff38175b088fdec3b05d52 | Python | 381 | 16 | from .fno import TFNO, FNO, ConditionalFNO
# only import SFNO if torch_harmonics is built locally
try:
from .sfno import SFNO
from .local_no import LocalNO
except:
pass
from .uno import UNO
from .uqno import UQNO
from .fnogno import FNOGNO
from .gino import GINO
from .codano import CODANO
from .rno import ... |
aaeed4002ac7de2cd783089523540b08c5ea9a29d690caf14f059444a5abbfbc | Python | 381 | 16 | # Copyright (c) Open-MMLab. All rights reserved.
import json
from .base import BaseFileHandler
class JsonHandler(BaseFileHandler):
def load_from_fileobj(self, file):
return json.load(file)
def dump_to_fileobj(self, obj, file, **kwargs):
json.dump(obj, file, **kwargs)
def dump_to_str(se... |
fe6f06b28e187b5e28f31fdd0fcfbe305e8b6c10e80c49c56efaff68800da416 | Python | 381 | 11 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
from numpy.testing import assert_array_equal
from eelbrain import load
from eelbrain.testing import requires_pyarrow, file_path
@requires_pyarrow
def test_feather_io():
ds = load.feather(file_path('mini.feather'))
assert_array_equal(ds['participant'], [... |
1e4b7aee89728ba9b26a99abb93a680bd69539c028a3bec78d9f072798c02e00 | Python | 382 | 15 |
from torch.utils.ffi import _wrap_function
from ._roi_crop import lib as _lib, ffi as _ffi
__all__ = []
def _import_symbols(locals):
for symbol in dir(_lib):
fn = getattr(_lib, symbol)
if callable(fn):
locals[symbol] = _wrap_function(fn, _ffi)
else:
locals[symbol] =... |
62c50c1a06dbb66dbab0d38f1a8c866fa7d898c1be5f3cac0a6a392ef9543ecc | Python | 382 | 8 | custom_params = {}
custom_params['cache_dir'] = 'nn_models/sc05/cache/'
custom_params['model_dir'] = 'nn_models/sc05/all_feat/model_data/'
custom_params['desc_dir'] = 'output/sc05/all_feat/model_data/'
custom_params['gif_eval_out'] = 'nn_models/sc05/gif_eval/'
custom_params['min_sc_filt'] = 0.5
custom_params['max_sc_fi... |
b8b2cd3130fe50845585a3ecb27d2d8062081d495f1022fa240ee06bdc61a3c0 | Python | 382 | 21 | """Local adapter"""
import os
def get_device_id():
device_id = os.getenv('DEVICE_ID', '0')
return int(device_id)
def get_device_num():
device_num = os.getenv('RANK_SIZE', '1')
return int(device_num)
def get_rank_id():
global_rank_id = os.getenv('RANK_ID', '0')
return int(gl... |
c8a6fd2f38cd3bd9dde411556c11b56cf655240e26eca2284094f51b667f623c | Python | 382 | 17 | from dataclasses import dataclass
from ontoportal_client import AgroPortalClient
from oaklib.implementations.ontoportal.ontoportal_implementation_base import (
OntoPortalImplementationBase,
)
@dataclass
class AgroPortalImplementation(OntoPortalImplementationBase):
"""
Implementation over agroportal endp... |
31dc6cfb74ca979e64e2e4af5d0d94e52490cb36134eba22755d0b96d04ffe7e | Python | 383 | 15 |
from torch.utils.ffi import _wrap_function
from ._roi_align import lib as _lib, ffi as _ffi
__all__ = []
def _import_symbols(locals):
for symbol in dir(_lib):
fn = getattr(_lib, symbol)
if callable(fn):
locals[symbol] = _wrap_function(fn, _ffi)
else:
locals[symbol] ... |
345a707b987fdb9b060bb5549e58040bedf6b937e5262f3bf6195c9b2dc26064 | Python | 383 | 16 | default_graph_info = {
"edges_per_point": 10,
"scale_types": ["step_std"],
"edge_method": "geom_causal",
"data_type": "no_features",
}
default_ds_params = {
"dim":2,
"noise_range": (0.02, 0.05),
"RW_types": ["fBM", "LW", "sBM", "OU", "CTRW"],
"length_range": (7, 35),
"logdiffusion_r... |
04d9314d791e2c7bf28e62a20df1242f7894804d57f906d5cc97767b3a986b66 | Python | 385 | 16 | #!/usr/bin/env python
import sys
from setuptools import setup
import versioneer
SETUP_REQUIRES = ["setuptools >= 30.3.0"]
SETUP_REQUIRES += ["wheel"] if "bdist_wheel" in sys.argv else []
if __name__ == "__main__":
setup(
name="phys2bids",
setup_requires=SETUP_REQUIRES,
version=versioneer.... |
1e68f5c65293aec9cf26bbcd5b4f09ba5bca035793362725fa2a7cf4b88c1c16 | Python | 385 | 15 |
from torch.utils.ffi import _wrap_function
from ._roi_pooling import lib as _lib, ffi as _ffi
__all__ = []
def _import_symbols(locals):
for symbol in dir(_lib):
fn = getattr(_lib, symbol)
if callable(fn):
locals[symbol] = _wrap_function(fn, _ffi)
else:
locals[symbol... |
f175de40371d37d54a56f3bf5f5b94223a3aa277e31576ebb30a7c987c54a1b1 | Python | 385 | 17 | #!/usr/bin/env python
import sys
from setuptools import setup
import versioneer
SETUP_REQUIRES = ["setuptools >= 65.5.1"]
SETUP_REQUIRES += ["wheel"] if "bdist_wheel" in sys.argv else []
if __name__ == "__main__":
setup(
name="phys2cvr",
setup_requires=SETUP_REQUIRES,
version=versioneer.... |
35a2489b4ef80312725b12d33145203c2682fd6706db0fdbd8a29503ebc80883 | Python | 386 | 17 | # physical constants and unit convertion
accepted_units = [
"ev",
"kcalmol",
]
calories_to_joule = 4.184
hartree_to_ev = 27.211386245988
hartree_to_kcalmol = 627.5094740631
ev_to_kcalmol = hartree_to_kcalmol / hartree_to_ev
kcalmol_to_ev = hartree_to_ev / hartree_to_kcalmol
# Physical constants
k_kcalmolkel... |
232d8d8ebea927287c6c0b846ddf4cb89c5eb04a372b02578ad2a9d51c2cf72a | Python | 387 | 17 | #!/usr/bin/env python3
import glob
import os
import sys
if len(sys.argv) != 2:
print ("Usage: {} <noises-dir>".format(sys.argv[0]))
raise SystemExit(1)
for line in glob.glob("{}/*.wav".format(sys.argv[1])):
fname = os.path.basename(line.strip())
print ("--noise-id {} --noise-type point-source "
... |
2e8ac96ccfbdc3174f7e961966482e89107688ffc16324af0b5050692dfccf45 | Python | 389 | 15 | import torch
from src.dataset import LargeRandomSampler
class TestLargeRandomSampler:
test_data_path = "data/test_data/"
def test_initilization(self):
dataset = range(1000)
generator = torch.Generator()
generator.manual_seed(14)
sampler = LargeRandomSampler(
datas... |
be1767b32c2c75dae8121368003ec3757f553f695d4d232356e04713d4cdd8dc | Python | 390 | 12 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import os
def get_config_root_path():
''' Path to configs for unit tests '''
# cur_file_dir is root/tests/env_tests
cur_file_dir = os.path.dirname(os.path.abspath(os.path.realpath(__file__)))
ret = os.path.dirname(os.path.dirname... |
d3372b39f78062beb346ab9c9263a61c1b898b6c67be4b4ec28030c7860d860f | Python | 390 | 11 | """Weighted Matthews correlation coefficient — vendored component.
Vendored from https://github.com/fabiom91/MCC_Weighted at tag v1.0.0
(commit ed55752f5af1bf704d11b7159904fa7a55857227). See README.md in this
directory for provenance and licence.
"""
from .weighted_metrics import Weighted_metrics, weights # noqa: F4... |
e2b2282cb679e09a8074ea88cdd72eb0bc3828ca8723fc2aecc924983082b023 | Python | 390 | 18 | __author__ = "Jianfeng Sun"
__version__ = "v1.0"
__copyright__ = "Copyright 2023"
__license__ = "GPL v3.0"
__email__ = "jianfeng.sunmt@gmail.com"
__maintainer__ = "Jianfeng Sun"
def Color(color, sel_name):
"""PyMOL color a chain"""
from pymol import cmd
cmd.color(color=color, selection=sel_name)
# c... |
6f13318c6a4bad6f81e30874849b1d7f5c9aede57ffb6561a027859e1a865bfb | Python | 391 | 18 | """A recipe with a faulty regexp for testing preprocessor functionality."""
UNACCEPTED_CHARS = r"[^a-zåäöA-ZÅÄÖ \n]"
REGEXPS = [
# Remove punctuation
(r"[.,!?':]", r""),
# Faulty regexp with an unmatched parenthesis
(r"(the", r""),
# Strip any remaining extra spaces
(r"^\s+", r""),
(r"\s+"... |
7f6a3b21f47b60dee48da6b8175c8d1868e62a14d7302cba816320593659ac1a | Python | 391 | 14 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Variational Animal Motion Embedding 0.1 Toolbox
© K. Luxem & P. Bauer, Department of Cellular Neuroscience
Leibniz Institute for Neurobiology, Magdeburg, Germany
https://github.com/LINCellularNeuroscience/VAME
Licensed under GNU General Public License v3.0
"""
import ... |
04ad17082ccbe9ee80e92d9855e5e02f1ce95972e7b437287ebd3a359728109a | Python | 393 | 16 | import PyQt5
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *
from PyQt5.QtGui import *
class PopupMessage(QMessageBox):
def __init__(self, parent=None):
QMessageBox.__init__(self)
self.setWindowTitle("Caution!")
self.setText('Histological image: is oversized.')
button = ... |
e3fdb9e74d67ea948f648b7d3938bd8dc6fd57058ab2e1b595acec198c4f15a1 | Python | 393 | 18 | project = 'QUIT'
copyright = '2018-, Tobias Wood'
author = 'Tobias Wood'
language = 'en'
version = ''
release = ''
extensions = [
'sphinx.ext.mathjax',
]
templates_path = ['_templates']
source_suffix = '.rst'
master_doc = 'index'
exclude_patterns = ['_build', 'External', 'Thumbs.db', '.DS_Store']
pygments_style =... |
1d6b1e7deecedb426fc23a9821f45c7a6df758505dff14fc97ba36eef1283040 | Python | 395 | 21 | """
__init__.py
Created by: Martin Sicho
On: 06.04.22, 16:51
"""
import torch
from rdkit import rdBase
from .about import VERSION
rdBase.DisableLog('rdApp.error')
rdBase.DisableLog('rdApp.info')
rdBase.DisableLog('rdApp.warning')
torch.set_num_threads(1)
DEFAULT_DEVICE = torch.device('cuda') if torch.cuda.is_avail... |
362f4cf0144e45efbd814d9807fdcb16a82b1aa0bf12fc9c963989ea1b760a06 | Python | 395 | 14 | import vedo
verts = [(50,50,50), (70,40,50), (50,40,80), (80,70,50)]
faces = [(0,1,2), (2,1,3), (1,0,3)]
# (the first triangle face is formed by vertex 0, 1 and 2)
# Build the polygonal Mesh object:
mesh = vedo.Mesh([verts, faces])
mesh.backColor('violet').lineColor('tomato').lineWidth(2)
labs = mesh.labels('id')
p... |
081df935aeb5a60a2ea42d1d5c9d48729543bc4b0568ff64ca75ea239259840f | Python | 397 | 18 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
import os
from eelbrain import *
import mne
PATH = 'test-ica.fif'
ds = datasets.get_mne_sample()
ds['epochs'] = ds['epochs'].pick_types('mag')
if os.path.exists(PATH):
ica = mne.preprocessing.read_ica(PATH)
else:
ica = mne.preprocessing.ICA(0.95)
... |
4a2317fea80fbfc5d43fb12962d0cd5999c09254271a0c2ab6185b5bd42d5dd5 | Python | 398 | 19 | import sys
class Logger(object):
def __init__(self, filename="logfile.txt"):
super().__init__()
self.terminal = sys.stdout
self.log = open(filename, "a")
def write(self, message):
self.terminal.write(message)
self.log.write(message)
def flush(self):
""""""... |
4e803adc89da604caa02bf950eb1f65eafc3d2666145319f6b4f1858135fce8f | Python | 399 | 17 | __author__ = "Johannes Köster"
__copyright__ = "Copyright 2016, Johannes Köster"
__email__ = "johannes.koester@protonmail.com"
__license__ = "MIT"
from snakemake.shell import shell
log = snakemake.log_fmt_shell()
shell(
"picard CollectInsertSizeMetrics {snakemake.params} "
"INPUT={snakemake.input} OUTPUT=... |
7422287728056aa8388a1ccc5dd810d8d7914dae43642efbe86a774fdec8e4d3 | Python | 399 | 19 | __author__ = "Jan Forster"
__copyright__ = "Copyright 2019, Jan Forster"
__email__ = "j.forster@dkfz.de"
__license__ = "MIT"
from snakemake.shell import shell
## Extract arguments
extra = snakemake.params.get("extra", "")
log = snakemake.log_fmt_shell(stdout=True, stderr=True)
shell(
"( bedtools merge"
" {e... |
d31f9e5f90f2e49ca7ef564ec519c5422bcceecd8a86397d81f069e35a68b286 | Python | 399 | 12 | # Copyright (c) Open-MMLab. All rights reserved.
from .base import LoggerHook
from .mlflow import MlflowLoggerHook
from .pavi import PaviLoggerHook
from .tensorboard import TensorboardLoggerHook
from .text import TextLoggerHook
from .wandb import WandbLoggerHook
__all__ = [
'LoggerHook', 'MlflowLoggerHook', 'PaviL... |
06e3d47f61d9959270e9082d5364bc8d4d1d6d9c657abb953663a606487a329e | Python | 400 | 17 | import unittest
from deeprank_gnn.DataSet import HDF5DataSet, DivideDataSet, PreCluster
from deeprank_gnn.tools.BSA import BSA
class TestBSA(unittest.TestCase):
def setUp(self):
self.bsa = BSA('tests/data/pdb/1ATN/1ATN_1w.pdb')
def test_structure(self):
self.bsa.get_structure()
self.... |
b07e7bf1a4f1315306343bb8e472bfeaf0ad42b153f56e74fa2375b5c0e4a6eb | Python | 400 | 16 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Miscellaneous utility functions
"""
import torch
def cat(tensors, dim=0):
"""
Efficient version of torch.cat that avoids a copy if there is only a single element in a list
"""
assert isinstance(tensors, (list, tuple))
if ... |
f5160fa77b5419012ffd0e182edef847d4e5356aa6e735b10b7f1f3d778613d2 | Python | 400 | 12 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from maskrcnn_benchmark.utils.registry import Registry
BACKBONES = Registry()
RPN_HEADS = Registry()
ROI_BOX_FEATURE_EXTRACTORS = Registry()
ROI_BOX_PREDICTOR = Registry()
ROI_KEYPOINT_FEATURE_EXTRACTORS = Registry()
ROI_KEYPOINT_PREDICTOR = Regi... |
0b4a7f864865595f37280adb9395487eb2602e7185bc687b0553e504507462fd | Python | 401 | 18 | config = {
'EXP_NAME': "PCovNet_phase2_covid",
'EXP_PHASE': "phase2",
'EXP_GROUP': "covid",
'AUGMENT_DATA': True,
'ONLY_NIGHT_DATA': False,
'MODEL_DIR': None,
'TRAIN_VAE': True,
'TRAIN_LSTM': True,
'LEN_WIN': 8,
'N_WIN': 2,
'LATENT_DIM': 16,
'BATCH_SIZE': 64,
'VAL_SPL... |
5b42c5ae1258e679ab05ee9d3505ae9e95c1bfa8d224241ed0efdf3ff0cc4415 | Python | 402 | 21 | import gzip
import sys
## converts SNPMappings.txt.gz to bed
infile = sys.argv[1] # SNPMappings.txt.gz file
outfile = sys.argv[2] # bed out
fh = gzip.open(infile,'rt')
fho = open(outfile,'w')
for line in fh:
# dostuff
# 1_10497_C_T_b37
line = line.rstrip()
elems = line.split("\t")
fho.write("chr"+elems[0]+"\t... |
80e712d13b385116ec444a5974ecce68e5321fa142107fc4241d3a476cf3f8d6 | Python | 402 | 15 | __author__ = "Jianfeng Sun"
__version__ = "v1.0"
__copyright__ = "Copyright 2023"
__license__ = "GPL v3.0"
__email__ = "jianfeng.sunmt@gmail.com"
__maintainer__ = "Jianfeng Sun"
class Select:
def chain(self, chain_name, chains="A+B+C+D+E+F+G+H+I+J+K"):
"""PyMOL select a chain"""
from pymol import ... |
b483d2df3d16e000cdfbd1ccdd37668ec6e9e31b7d715d8efd46bd5f77b935e9 | Python | 402 | 20 | from .segment import (
SegmentationStrategy,
CircularityFilter,
PlaneWiseSegmentation,
ROIFilter,
PostProcessingStep,
SmoothROIBoundaries,
MergeOverlapping,
SparseROIsFromMasks,
)
from .stica import STICA
from .ca1pc import (
PlaneCA1PC,
AffinityMatrixCA1PC,
)
from .normcut impor... |
298613c846a8c47f340dab6e73fe2f3fab18b705f553764276ed9329637831a1 | Python | 403 | 12 | import torchvision.transforms.functional as tvf
def differentiable_clip_preprocess_from_stylegan(img, clip_visual_size):
img = tvf.resize(
img, size=clip_visual_size, interpolation=tvf.InterpolationMode.BICUBIC
)
img = (img + 1) / 2 # -1~1 to 0~1
img = tvf.normalize(
img, (0.48145466,... |
bea82bfa2d876af5105cb02e392b6517ddfa7b2a4b1ea824c15f90e562089eaf | Python | 403 | 15 | from .bfp import BFP
from .fpn import FPN
from .fpn_carafe import FPN_CARAFE
from .hrfpn import HRFPN
from .nas_fpn import NASFPN
from .nasfcos_fpn import NASFCOS_FPN
from .pafpn import PAFPN
from .rfp import RFP
from .caps_pafpn import CAPS_PAFPN
from .Caps_fpn import Caps_FPN
__all__ = [
'FPN', 'BFP', 'HRFPN', '... |
ab10dea23daf31272905e66f460ed6e1c7777af044b810efb9bef4485214ec43 | Python | 404 | 18 | import mesh
import FBEM
radii = list(range(1, 5))
def run_simulation(folder, size):
sphere = mesh.sphere_create('sphere', radius=size, velocity=(1, 0, 0), grid=3)
FBEM.run(sphere, folder, cnd_path='input2018.cnd')
def run_sequential(radii):
for radius in radii:
folder = 'spheres/expected/radius... |
14f103f956a3bc21a620684c2d726b3317e68cbf414d360d3108f6216f89e939 | Python | 407 | 15 | from snakemake.shell import shell
import os.path
from os import path
log = snakemake.log_fmt_shell(stdout=True, stderr=True)
extra = snakemake.params.get("extra", "")
family = snakemake.wildcards.family
dir = os.path.dirname(snakemake.output[0])
prefix = path.join(dir, family) # qc/peddy/412
shell(
"peddy {sna... |
27e79cae63c95ce9ef11981a71485acdb0f4e4cf97d4e8827e17194d30588823 | Python | 407 | 15 | import numpy as np
import numpy.testing as npt
import AFQ.data.fetch as afd
import nibabel as nib
def test_read_roi():
aff1 = np.eye(4)
template = nib.Nifti1Image(np.ones((10, 10, 10)), aff1)
aff2 = aff1[:]
aff2[0, 0] = -1
roi = nib.Nifti1Image(np.zeros((10, 10, 10)), aff2)
img = afd.read_res... |
c9f6efcd89b4163524996ce2e3b6fa3f7fa7db08fe1d76dae799bafccc2a5e65 | Python | 407 | 16 | """Build fast_coco_eval library"""
import sys
from setuptools import setup
from pybind11.setup_helpers import Pybind11Extension
args = ["-O2"] if sys.platform == "win32" else ["-O3", "-std=c++14", "-g", "-Wno-reorder"]
ext_modules = [
Pybind11Extension(
name="fast_coco_eval",
sources=['cocoeval/co... |
c873e8683031dbbc7db357d755404f045fc16368a5d2549b85b7c9c7f078a3b5 | Python | 409 | 12 | from .base import *
# from .default_localizer import DefaultLocalizer
from .drift_corrector import CorrelationDriftCorrector, BeadDriftCorrector
from .trackpy_tracker import ConservativeTracker
from .window_percentile import WindowPercentileFilter
from .quot_localizer import (
MaxLikelihoodLocalizer,
BaseDetec... |
cae1136f9faa7acebaedcefa639503ea43670008066ff65a97ef7b508a5d31fc | Python | 409 | 12 | # optimizer
#optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001) #weihgt_decay L2 正则化的权重衰减系数
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iter... |
13aa87ad1777342f020924189dec4d359a4e67c6b74c38bb764b0b7cf5ba9432 | Python | 410 | 12 | """Device adapter for ModelArts"""
from src.model_utils.config import config
if config.enable_modelarts:
from src.model_utils.moxing_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
else:
from src.model_utils.local_adapter import get_device_id, get_device_num, get_rank_id, get_job_... |
b73f7bee9689dc24688a1890aed6a06d18bfb031d361d318c499b3724001f64a | Python | 411 | 18 | __author__ = "David Laehnemann, Victoria Sack"
__copyright__ = "Copyright 2018, David Laehnemann, Victoria Sack"
__email__ = "david.laehnemann@hhu.de"
__license__ = "MIT"
import os
from snakemake.shell import shell
prefix = os.path.splitext(snakemake.output[0])[0]
shell(
"samtools bam2fq {snakemake.params} "
... |
f6cc60e7c6fc884b9d424a895e3a15f1de79bc69af68b6ed1096736fbe222aa9 | Python | 411 | 17 | __author__ = "Johannes Köster"
__copyright__ = "Copyright 2016, Johannes Köster"
__email__ = "johannes.koester@protonmail.com"
__license__ = "MIT"
from snakemake.shell import shell
log = snakemake.log_fmt_shell()
shell(
"picard CollectAlignmentSummaryMetrics {snakemake.params} "
"INPUT={snakemake.input.ba... |
ffd10b17ebf944e9aaecc8b6d1a770a15e425f5e312a3f5bf3d8bff9e2b7c0e6 | Python | 413 | 24 | # -*- coding: utf-8 -*-
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import .boxer
@click.command()
def boxer():
pass
@boxer.command("create")
def create():
boxer = boxer.BoxerModel();
boxer.save("boxer.h5")
@boxer.command("train")
def train():
boxer = boxer.BoxerModel();
boxer.load("boxer... |
e9e484b6b00b5fad6ca81a49a9281cb65951f0da4312203454625e4a8fa03a8d | Python | 414 | 14 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Variational Animal Motion Embedding 0.1 Toolbox
© K. Luxem & P. Bauer, Department of Cellular Neuroscience
Leibniz Institute for Neurobiology, Magdeburg, Germany
https://github.com/LINCellularNeuroscience/VAME
Licensed under GNU General Public License v3.0
"""
import ... |
2151d73f88e26e2ae57a7040ca0d7ae157e2779dd19c4bfa34cbf6fb23454932 | Python | 417 | 24 | """
collectors
Created by: Martin Sicho
On: 14.07.22, 10:58
"""
from drugex.parallel.interfaces import ListCollector
class ListExtend(ListCollector):
def __init__(self):
self.items = []
def __call__(self, result):
self.items.extend(result)
def getList(self):
return self.items
... |
13f1e4634cfc53140bdee03a87f6c15352fb04d6727ff076e3a534139b5953b3 | Python | 418 | 18 | import cv2
import numpy as np
import paddle
def cv2paddle(img):
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.transpose(img, (2, 0, 1))
img = paddle.to_tensor(img, dtype='float32').unsqueeze(axis=0)
img = img / 255.0
return img
def paddle2cv(img):
img = img.numpy()[0]
img = np.trans... |
57ef4e876066b8315094a4c2625ee4f83d9f133a448d1a1b94918972a08f111f | Python | 418 | 12 | from snakemake.shell import shell
log = snakemake.log_fmt_shell(stdout=True, stderr=True, append=True)
family = snakemake.wildcards.family
outdir = snakemake.params.outdir
tool = snakemake.params.tool
pythonpath = tool.replace("bin", "")
python = " export PYTHONPATH={pythonpath}; "
mity = " {tool}/mity call --prefix... |
0c98f8b36ca119256a58325df9dd284167f913f36a90233e5617d7fb45c87eb1 | Python | 419 | 12 | from .explanation import Explanation, EnclosingSubgraph
from .nx_conversion import khop_subgraph_nx, to_networkx_conv
from .random import check_random_state
from .misc import make_node_ref, distance, node_mask_from_edge_mask, top_k_mask
from .misc import threshold_mask, match_edge_presence, edge_mask_from_node_mask... |
0d91adf7add7d3a59f24af9f4ff2fd25f6a47544838eac702ebd1f9d4c94258e | Python | 419 | 12 | import os
from nilearn import plotting
from brainNetwork.util.path import get_data_dir
# Load example surface from the left hemisphere
f_name = "lh.gray_surface.rsl.gii"
example_surface = os.path.join(get_data_dir(), "template_surfaces", f_name)
view = plotting.plot_surf_contours(surf_mesh = example_surface,
... |
565a990a99db7aeed512c4ad331249411f6cbb0e2c159f8e8fc5fa00b9af6990 | Python | 419 | 17 | import os
import pickle
def pkl_save(path, obj, **kwargs):
"""Saves a serializable obj to a pickle (.pkl) file."""
path = os.path.splitext(path)[0] + '.pkl'
with open(path, 'wb') as outfile:
pickle.dump(obj, outfile, **kwargs)
def pkl_load(path, **kwargs):
"""Loads a pickled obj from path... |
a315084ebbb54b2a9b677d97992d493bdada70c2d00c94399feb90a62082e22a | Python | 419 | 18 | import gzip
import glob
import sys
f=sys.argv[1]
outfile=sys.argv[2]
with gzip.open(f,'rt') as input_file, gzip.open(outfile,'wt') as out:
x = 0
out.write(input_file.readline())
for line in input_file:
x += 1
if x % 1000000 == 0:
print(x)
fdr... |
427315ad5af6d07c4585f5b2ea0271c13abdd21f82719c8e43b3eaddace8bc14 | Python | 422 | 17 | import pandas as pd
import sys
def add_new_cluster_information(input, cluster, output):
pdf = pd.read_csv(input, header=0, index_col=0)
cdf = pd.read_csv(cluster, header=0)
df = pdf.merge(cdf, how='left', left_index=True, right_index=True)
if __name__ == "__main__":
assert len(sys.argv) >= 5
i... |
27b9fc48f42e97f81b5676ba67657f53e918eff1462df594ac5c797f7ef8a608 | Python | 423 | 17 | import os
def create_dirs(dirs):
"""
dirs - a list of directories to create if these directories are not found
:param dirs:
:return exit_code: 0:success -1:failed
"""
try:
for dir_ in dirs:
if not os.path.exists(dir_):
os.makedirs(dir_)
return 0
... |
f1e82556931ca76cec54ef8e9ac06842c67d6baed07f4acd8a3601ea7ce0b72d | Python | 423 | 18 | import sys
class Logger(object):
"""
This class enables logging to both stdout and text file
"""
def __init__(self, logfile_name, terminal=sys.stdout):
self.terminal = terminal
self.log = open(logfile_name, "w")
def write(self, message):
self.terminal.write(message)
... |
a38ed4e02bd0c2bf96ed6db75048e0633bd4a6a0baf2195350b9eb1f05310ab4 | Python | 424 | 21 | import os
import sys
f = open('log/parameters.txt', 'r')
lines = f.readlines()
bestf1 = 0
dicts = {}
for line in lines:
conts = line.strip().split(',')
k = int(conts[0].split(':')[1][1:])
p = float(conts[1].split(':')[1][1:])
alpha = float(conts[2].split(':')[1][1:])
f1 = float(conts[3].split(':')[1][1:])
dict... |
31f2773df4fcf1a061f1bf9ae7f0a954d7e851a471631fa8b44c66486834d767 | Python | 425 | 24 | from dataclasses import dataclass
from oaklib.implementations.sparql.abstract_sparql_implementation import AbstractSparqlImplementation
from oaklib.interfaces.owl_interface import OwlInterface
@dataclass
class OwleryImplementation(OwlInterface, AbstractSparqlImplementation):
"""
Wraps an owlery endpoint
... |
5ae2f9866ae8c7c5d645e284cd37575a296b19ce6ea5c70ad184baa1fb76a792 | Python | 427 | 21 | import os
import json
class Predictor:
def __init__(self):
self.batch_size = 128
os.system("chmod +x work")
def predict(self, content):
f = open("input.txt", "w")
print(json.dumps(content, ensure_ascii=True), file=f)
f.close()
os.system("./work")
f = ... |
d0c9f634ee3f80302253cee7209a2d36a21d139b609da2e2d589f2cbc1137221 | Python | 427 | 19 | from dataclasses import dataclass
from oaklib.implementations.sparql.abstract_sparql_implementation import (
AbstractSparqlImplementation,
)
from oaklib.interfaces.owl_interface import OwlInterface
@dataclass
class OwleryImplementation(OwlInterface, AbstractSparqlImplementation):
"""
Wraps an owlery endp... |
967beca2c376b7fcf477a042e5b96e878e9469622d11f5c5307e4e41c4e93b42 | Python | 428 | 16 | #!/usr/bin/env python2.7
#coding:utf-8
from __future__ import print_function
import sys
from mmseg import seg_txt
for line in sys.stdin:
blks = str.split(line)
out_line = blks[0]
for i in range(1, len(blks)):
if blks[i] == "[VOCALIZED-NOISE]" or blks[i] == "[NOISE]" or blks[i] == "[LAUGHTER]":
out_line... |
3c82a4f156a2dddd26c89e858088da795b7bccc88ab2cf4ecec453c57b4a2fca | Python | 430 | 20 | __author__ = "Johannes Köster"
__copyright__ = "Copyright 2018, Johannes Köster"
__email__ = "johannes.koester@protonmail.com"
__license__ = "MIT"
from snakemake.shell import shell
extra = snakemake.params.get("extra", "")
log = snakemake.log_fmt_shell(stdout=False, stderr=True)
shell(
"picard "
"CreateSeq... |
08bf8abc77f4bb782759c8e2c1de24eea3bdc3f073d18e1407549cea0cf0407d | Python | 431 | 30 | from collections import namedtuple
Genotype = namedtuple('Genotype', 'backbone rpn')
OP_NAMES = [
'sep_conv_3x3',
'sep_conv_3x3_dil3',
'sep_conv_5x5_dil6',
'skip_connect',
'def_conv_3x3',
]
AGG_NAMES = [
'psum',
'cat'
]
HEAD_OP_NAMES = [
'conv1x1',
'conv3x3',
'sep_conv_3x3',
... |
dc5e9214b0cf89f4b4c7f6e71297c3329e375b806f50555cedc7a33412186285 | Python | 431 | 13 | import os
import gdown
os.makedirs('./saved_models/u2net', exist_ok=True)
os.makedirs('./saved_models/u2net_portrait', exist_ok=True)
gdown.download('https://drive.google.com/uc?id=1ao1ovG1Qtx4b7EoskHXmi2E9rp5CHLcZ',
'./saved_models/u2net/u2net.pth',
quiet=False)
gdown.download('https://drive.google.com/uc?i... |
a8cc15c6b7fbed43fc1504ff1b26218deb71c9dea2b277d73951f590fe072480 | Python | 432 | 17 | from snakemake.shell import shell
import os
log = snakemake.log_fmt_shell(stdout=True, stderr=True)
prefix = os.path.splitext(snakemake.output.json)[0].split(".")
shell(
"sex={snakemake.params.sex} && "
"( ExpansionHunter"
" --reference {snakemake.params.ref}"
" --reads {snakemake.input.bam}"
" -... |
2bc6fa0763d10a258e82cc8e2167a3d733664a8df723028a3381fcd19353004b | Python | 433 | 11 | from .volumes import Volume
from .neurons import Neuron, BaseNeuron, TreeNeuron, MeshNeuron, Dotprops
from .neuronlist import NeuronList
from .core_utils import make_dotprops, to_neuron_space, NeuronProcessor
from typing import Union
NeuronObject = Union[NeuronList, TreeNeuron, BaseNeuron, MeshNeuron]
__all__ = ['Vo... |
6fc1521e73326932d876d7b51ed9c0c530bee7c64b40b7957b76a54cbaef7b0d | Python | 433 | 19 | """Utility module"""
def get_data_dir():
"""
Get the main directory for data
"""
return "/Users/karimia/data/brainnet_data/Figures_Tables/Fig_1"
def get_fig_dir():
"""
This is where we save the figures
"""
return "/Users/karimia/code/Brain_Network/python/figures"
def get_local_datadir... |
15a5a775f51b43ab32be4011fdf977b99c59f97c35a2fa393d18797a037dfb32 | Python | 434 | 20 | import torch
import networkx as nx
import matplotlib.pyplot as plt
from torch_geometric.utils import to_networkx
from graphxai.datasets.real_world.MUTAG import MUTAG
mutag = MUTAG(root = '.')
g1 = mutag.graphs[109]
print(torch.sum(g1.x, dim = 0))
node_c = [g1.x[i,1].item() for i in range(g1.x.shape[0])]
print(node... |
71e89ddd3fa718fc88c8f2755e7568298ebc3a07eba3f8052a2d3109e4ef3073 | Python | 434 | 14 | '''
Extract data: velocities, forces, etc.
[Folder sphere_presaved contains output expected after step00 in case the user is not able to run step00]
'''
import FBEM
folder = 'data/sphere_presaved/'
res = FBEM.extract_all_data(folder) # output ResultsData class
coords = res.coordinates
velocities = res.velocities # ... |
7228c8c42f01dc13e40dd461850f92546dc5e99ae0a9915c9a8e888c999254ca | Python | 436 | 20 | __author__ = "Jan Forster"
__copyright__ = "Copyright 2019, Jan Forster"
__email__ = "j.forster@dkfz.de"
__license__ = "MIT"
from snakemake.shell import shell
## Extract arguments
extra = snakemake.params.get("extra", "")
log = snakemake.log_fmt_shell(stdout=True, stderr=True)
shell(
"(bedtools slop"
" {ext... |
7d840b575bf31bf63fcffb125165317bab7ef733b9cf7b7426b1246da2768f78 | Python | 436 | 12 | from .subgraphx import SubgraphX
from .cam import CAM, GradCAM
from .guidedbp import GuidedBP
from .gnn_lrp import GNN_LRP
from .random import RandomExplainer
from .grad import GradExplainer
from .integrated_grad import IntegratedGradExplainer
from .graphlime import GraphLIME
#from .gnn_explainer import GNNExplainer
fr... |
927e79fdd3ff3407340b83c87dff7ba32e0b4e274bcbc84605e4e25dcde1010f | Python | 437 | 17 | import sys, os
import torch
import networkx as nx
from graphxai.datasets.load_synthetic import Owen_root
from graphxai.datasets.utils.shapes import house
from graphxai.datasets.utils.verify import verify_motifs
assert len(sys.argv) == 2, 'usage: python3 check_graph.py <filename>'
file = sys.argv[1]
bah = torch.load(... |
18140f02b151db60cd475b91865b181efd2241972d54d51b485d638a17d9fa6c | Python | 438 | 17 | from .core import TrajectoryDataset, LabelFunction, Augmentations
from .fly_v1 import FlyV1Dataset
from .mouse_v1 import MouseV1Dataset
dataset_dict = {
'fly_v1' : FlyV1Dataset,
'mouse_v1' : MouseV1Dataset
}
def load_dataset(data_config):
dataset_name = data_config['name'].lower()
if dataset_nam... |
27806dad448cc7a69b3233c91298ec7bc2223dfa71f0288e99457c571c8679e2 | Python | 438 | 13 | import numpy as np
def softmax(x):
"""Compute softmax values for each sets of scores in x."""
e_x = np.exp(x - np.max(x))
return e_x / e_x.sum()
def uniform_vector(length):
return np.ones((length))/length
def make_initial_prior(initial_image_prior,initial_rationality_prior,initial_speaker_prior):
ret... |
8ed672f5dfb327489b8f1593d98ea5ac19739cda6bdd46bf0857191b50aa80a1 | Python | 438 | 14 | # Copyright (c) 2026 Computer Vision Center (CVC) at the Universitat Autonoma de
# Barcelona (UAB).
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
from . import SmokeTest
class TestClient(SmokeTest):
def test_version(self):
print("T... |
e229505bb8820c1078872f8a98d95bfb23e0712e611fdbfd7f6331d2b1b8dc03 | Python | 438 | 15 | import json
def readConfig(path):
with open(path,'r') as cf:
nodelocaltionstr = cf.read()
nodelocaltioninf = json.loads(nodelocaltionstr)
if isinstance(nodelocaltioninf,str):
nodelocaltioninf = json.loads(nodelocaltioninf)
return nodelocaltioninf
def writeConfig(path, inf... |
666497063842b91ee875d10688a6663fd7e9501e901c933b0b7457178429bf93 | Python | 439 | 14 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
import pytest
pytest.register_assert_rewrite('eelbrain.testing._testing')
from ..datasets._simple import get_ndvar
from ._testing import (
gui_test,
requires_mne_sample_data, requires_pyarrow, requires_r_ez,
skip_on_windows,
TempDir, working_dir... |
74f4e4b4f436d5f5a42b8b0ca2cc621df2c7cceeb049400465602bf0970ae993 | Python | 439 | 19 | #!/usr/bin/env python
lexicon = open("data/local/tmp/lexicon.1")
lexicon_ext = open("data/local/tmp/lexicon.2", "w")
for line in lexicon:
line = line.strip()
if "//" not in line:
lexicon_ext.write(line + "\n")
continue
lineComp = line.split("\t")
prons = lineComp[1].split("//")
for... |
8d8f3a014e341f863c2c0ceb262d117321a3a1316b7f76fce0480a87ae0a001e | Python | 439 | 10 | from __future__ import absolute_import
from . import utils
from . import paths
from . import setup_hd_gliomouse
print("\n########################")
print("If you are using hd-bet, please cite the following papers:\n")
print("UPCOMING\n")
print("Isensee, Fabian, et al. \"Automated Design of Deep Learning Methods for Bi... |
b82361620273332651e2f3b5ee10a8b4ed51ae14e14300a56bb2ff6514808905 | Python | 439 | 10 | from .anchor_generator import AnchorGenerator, LegacyAnchorGenerator
from .builder import ANCHOR_GENERATORS, build_anchor_generator
from .point_generator import PointGenerator
from .utils import anchor_inside_flags, calc_region, images_to_levels
__all__ = [
'AnchorGenerator', 'LegacyAnchorGenerator', 'anchor_insid... |
3dd9e92d39ab5341affa44c13ae80812ab49d607a7963ebec8d95a4ddff47a08 | Python | 440 | 18 | from setuptools import setup
setup(
name="PaiNN",
version="1.0.0",
description="Library for implementation of message passing neural networks in Pytorch",
author="xinyang",
author_email="xinyang@dtu.dk",
url = "https://github.com/Yangxinsix/PaiNN-model",
packages=["PaiNN"],
install_requ... |
418f5a0cd1866bf9f34717c598c3f96770b0fa3886a09b8f38961a3526d3c002 | Python | 441 | 21 | import torch.nn as nn
from .registry import ACTIVATION_LAYERS
@ACTIVATION_LAYERS.register_module()
class HSigmoid(nn.Module):
"""Hard Sigmoid Module. Apply the hard sigmoid function:
Hsigmoid(x) = min(max((x + 1) / 2, 0), 1)
Returns:
Tensor: The output tensor.
"""
def __init__(self):
... |
252f44926ff6b5fe3af38989feb86abfaf79526f0fc3114661d5d18fc6582e90 | Python | 442 | 16 | """Pipeline class implements functionality that is common to all processing pipelines."""
from logging import Logger
from typing import List
class Pipeline:
"""Pipeline template for different workflows."""
def __init__(self, log: Logger) -> None:
"""Initialize parameters common to all pipelines.
... |
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