sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
8a299618fad1be29c4c65575fbcb64c72c47204d9c02e5fd1418ba35f1faad87 | Python | 469 | 10 | import synapseclient
import synapseutils
syn = synapseclient.Synapse()
syn.login()
outdir = "data/mc_caa/"
# files = synapseutils.syncFromSynapse(syn, 'syn17010931', path = outdir + "Metadata/", ifcollision="keep.local")
# files = synapseutils.syncFromSynapse(syn, 'syn21547862', path = outdir + "Genetic_Variants/", ... |
8bf39d4f8d4a22b7457cd1e71a3b333c20c6dc7cee3b9bc72823bb6de7eb8c9e | Python | 469 | 18 | import os
import random
import numpy as np
import torch
def seed_everything(seed: int):
os.environ["PYTHONHASHSEED"] = str(seed)
random.seed(seed)
# Seed for NumPy's random number generation
np.random.seed(seed)
# Seed for PyTorch (both CPU and CUDA)
torch.manual_seed(seed)
if torch.cuda.... |
2264d7cbf9a753f5dfdc8dafdea1de019150afaf7544e0b7f28a0e0d9d30f885 | Python | 470 | 22 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
import common_setup
CONFIGS = {
'astar_lmcount': [
'--search',
'astar(lmcount(lm_merged([lm_rhw(),lm_hm(m=1)]),admissible=true,optimal=true),mpd=true)'],
}
exp = common_setup.IssueExperiment(
search_revisi... |
4bf9aad28f76f7b12c5d333136dfacf2a77d6d124899f0806515cbddbad9da03 | Python | 470 | 11 | import os
import torch
def save_model_w_condition(model, model_dir, model_name, accu, target_accu, log=print):
'''
model: this is not the multigpu model
'''
if accu > target_accu:
log('\tabove {0:.2f}%'.format(target_accu * 100))
# torch.save(obj=model.state_dict(), f=os.path.join(model... |
717ebf1216e48cc30d0c56209f395853d8e01049d18efadff8e25a0625c0d1da | Python | 470 | 25 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
from common_setup import IssueConfig, IssueExperiment
REVS = ["issue592-base", "issue592-v1"]
SUITE = suites.suite_optimal_strips()
CONFIGS = [
IssueConfig("seq-opt-bjolp", [], driver_options=["--alias", "seq-opt-bjolp"]),
]
exp = Issu... |
771d20f383e1f71601f725ffbe8a41bb5acd2702122354fd7bb66b7b9e647c69 | Python | 470 | 15 | import os
import pytest
import merlin
from merlin import merlin as m
@pytest.mark.fullrun
@pytest.mark.slowtest
def test_merfish_2d_full_local(simple_merfish_data):
with open(os.sep.join([merlin.ANALYSIS_PARAMETERS_HOME,
'test_analysis_parameters.json']), 'r') as f:
snakefilePa... |
a70419a9927f8a134befb43b558361f0f7fd0d518ab653aa3014522062d9d270 | Python | 470 | 22 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
import common_setup
CONFIGS = {
'astar_lmcount': [
'--search',
'astar(lmcount(lm_merged([lm_rhw(),lm_hm(m=1)]),admissible=true,optimal=true),mpd=true)'],
}
exp = common_setup.IssueExperiment(
search_revisi... |
b526ed7c8856d055d42e195d4e7dc23ff1edce55a4107d1c535b2c3832157ba1 | Python | 470 | 22 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
import common_setup
CONFIGS = {
'astar_lmcount': [
'--search',
'astar(lmcount(lm_merged([lm_rhw(),lm_hm(m=1)]),admissible=true,optimal=true),mpd=true)'],
}
exp = common_setup.IssueExperiment(
search_revisi... |
be0cfc6a28e70ed9063293bbda410b3bb115972e7089afd0bf980a20b0b66362 | Python | 470 | 25 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
from common_setup import IssueConfig, IssueExperiment
REVS = ["issue592-base", "issue592-v2"]
SUITE = suites.suite_optimal_strips()
CONFIGS = [
IssueConfig("seq-opt-bjolp", [], driver_options=["--alias", "seq-opt-bjolp"]),
]
exp = Issu... |
d861d709c8cb195241d7323382d4a14e4d9b2c1e6c3914ef1f402eb60cf607e3 | Python | 470 | 25 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
from common_setup import IssueConfig, IssueExperiment
REVS = ["issue592-base", "issue592-v3"]
SUITE = suites.suite_optimal_strips()
CONFIGS = [
IssueConfig("seq-opt-bjolp", [], driver_options=["--alias", "seq-opt-bjolp"]),
]
exp = Issu... |
e001497f955be7967796c2951f0c7bb2a5beef30d3c15e7ec3849d69559f64ba | Python | 470 | 17 | # This file is autogenerated by the command `make fix-copies`, do not edit.
from ..file_utils import requires_backends
class ImageFeatureExtractionMixin:
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class DeiTFeatureExtractor:
def __init__(self, *args, **kwargs):
... |
f76a5b36040060c86f5976f90fa9f2771485e89ed11181cfa5a038962bf07783 | Python | 470 | 22 | # set QT_API environment variable
import os
import sys
os.environ["QT_API"] = "pyqt5"
import qtpy
# qt libraries
from qtpy.QtCore import *
from qtpy.QtWidgets import *
from qtpy.QtGui import *
# app specific libraries
import control.gui_tiscamera_simulation as gui
#import control.gui_2cameras_async as gui
#import co... |
858f9ffc2a8ce026e213663e32aef5115ea478ff5f7f963546a51eca0b43559e | Python | 471 | 22 | import pytest
@pytest.mark.xfail
def test_that_you_wrote_tests():
from textwrap import dedent
assertion_string = dedent(
"""\
No, you have not written tests.
However, unless a test is run, the pytest execution will fail
due to no tests or missing coverage. So, write a real test and
t... |
8a2056de38dff59235f691c803758292ff31b315f971ca27916175622f638e9a | Python | 471 | 10 | from ..utils import IKFastInfo
from ..ikfast import * # For legacy purposes
# TODO: deprecate this file
#FRANKA_URDF = "models/franka_description/robots/panda_arm.urdf"
#FRANKA_URDF = "models/franka_description/robots/hand.urdf"
FRANKA_URDF = "models/franka_description/robots/panda_arm_hand.urdf"
PANDA_INFO = IKFastI... |
aad2eee3bec76faad2127343516f72f18a7e2100a76b73c77b800b31091a13df | Python | 471 | 17 | import random
from .shape import Shape
class ShapeSet:
def __init__(self, shape_groups: list[list[Shape]]):
self.__shape_groups = shape_groups
def __len__(self):
return len(self.__shape_groups)
def __getitem__(self, idx):
if idx >= len(self.__shape_groups):
raise ... |
ce685c86c52b161d84c004ec9d9fd9b3a4578cb9e090cffb0444f56a4f4820be | Python | 471 | 21 | import unittest
class TestTowerMain(unittest.TestCase):
def test_imports(self):
try:
from dreamcoder.domains.tower.main import (
Flatten,
TowerCNN,
tower_options,
dreamOfTowers,
visualizePrimitives,
... |
22a2625e068d42ad658aacd643bc6964fc277432ed43aa0fbf0848ba6ffb4836 | Python | 472 | 23 | # SPDX-FileCopyrightText: 2024 Blue Brain Project / EPFL
#
# SPDX-License-Identifier: AGPL-3.0-or-later
'''
Compute rc densities for original and many samples of a control
Author(s): Daniela Egas S.
Last updated: 12.2023
'''
import json
import sys
sys.path.append('../../../library')
from structural_basic import *
de... |
44882fe3ad4a1158951e6572ac046645f7c4c2f8d5d22a81b9d62419e32286ab | Python | 472 | 25 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
from common_setup import IssueConfig, IssueExperiment
REVS = ["issue481-base", "issue481-v1"]
SUITE = suites.suite_satisficing_with_ipc11()
CONFIGS = [
IssueConfig("lama", [], driver_options=["--alias", "seq-sat-lama-2011"]),
]
exp = I... |
52a5fc81e467f14fea69d31d0ec979e4ab0b5ee65aef8d5f11a26f3cb4670362 | Python | 473 | 22 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
import common_setup
CONFIGS = {
'astar_lmcount': [
'--search',
'astar(lmcount(lm_merged([lm_rhw(),lm_hm(m=1)]),admissible=true,optimal=true),mpd=true)'],
}
exp = common_setup.IssueExperiment(
search_revisi... |
8cdf7d42f955d4fb28d5a014a6de8dc7bab3c73e174b14446173e0fb6285c54c | Python | 473 | 19 | from preprocessing import Preprocessor
from tokenization import Tokenizer
from config import Raviv1
from language import load_input_language
from learning_experiment import LearningExp
def test_preprocessing():
lang = load_input_language('S5')
prep = Preprocessor(Raviv1, lang)
lexp = LearningExp.load('da... |
a1a25f05a086d6e0bf4063ec364384def6d5686036fde7d2bea9cd450b68bb71 | Python | 473 | 13 | from cog import BaseRunner, File, Input, Path
class Runner(BaseRunner):
def run(
self,
image: Path = Input(description="An image path"),
document: File = Input(description="A file upload"),
# Optional variants
mask: Path | None = Input(description="Optional mask path", defa... |
b044ba7ac808d8b42831700923b5d99bcc1f6db427112c0767e657db779e148b | Python | 473 | 28 | from unittest import TestCase
import numpy as np
import matplotlib.pyplot as plt
from soundsig.basis import RadialBasis1D
class TestBasis(TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def test_radial_basis(self):
nsamps = 500
x = np.random.rand(nsamps)
... |
8263491b9915a8b4a540714a00f74052daddea066c19198496aab6e15ebab259 | Python | 474 | 18 | ## Enable from <module> import *
from os.path import dirname, basename, isfile, join
import glob
modules = glob.glob(join(dirname(__file__), "*.py"))
__all__ = [
basename(f)[:-3]
for f in modules
if isfile(f) and not f.endswith('__init__.py')
]
## Set up logging
from cuppa.logger import initialize_logging
... |
734254f2c41e91cdd171590ed1b800879335d23d9ba0927048d4014135d87099 | Python | 475 | 12 | from setuptools import setup, find_packages
setup(
name='synergy-3dmm',
version='0.0.1',
description='Library for accurate and fast 3d landmarks, face mesh, and face pose prediction',
packages=find_packages(exclude=('artistic*', 'benchmark*', 'loss_definition*', 'demo*', 'img*', 'pretrained*', 'Sim3DR'... |
8a5c02369fa700cc76560a7b5539a4bd480e27abd14a12e67240ddeb1d359343 | Python | 475 | 15 | from prettytable import PrettyTable
def count_parameters(model):
table = PrettyTable(["Modules", "Parameters"])
total_params = 0
for name, parameter in model.named_parameters():
if not parameter.requires_grad: continue
param = parameter.numel()
table.add_row([name, param])
... |
fd1e7ee7b993f9a20f62fcb6bc8c5cdc976eaca15449abcee05112703276b105 | Python | 475 | 22 | import time
import threading
class Temp:
def __init__(self, queue, is_values):
self.queue = queue
self.is_values = is_values
self.thr = threading.Thread(target=self.run,args=())
self.thr.start()
def run(self):
while True:
if self.queue.qsize() < 10:
self.queue.put(['measure','temperature_... |
19de2f1fd275cbb20036d87b62adec00c597a2e4565ce2fecb46b2f0ab70950b | Python | 477 | 14 | #!/Users/csmfindling/anaconda/bin/ python
from distutils.extension import Extension
from Cython.Distutils import build_ext
from distutils.core import setup
from Cython.Build import cythonize
import numpy
setup(
cmdclass = {'build_ext': build_ext},
ext_modules = [Extension("smc_c",
... |
e1305862dbe9e381819c05f0d5e21df584bef6beab2073538f041bac5bd10009 | Python | 478 | 18 | #!/bin/env python3
'''
Create figures for PDL topology paper introducing Feynman diagrams.
This file creates figures in the classical phase singularity view point.
'''
import yaml
import sys
import pdlfeynman as fy
yp = lambda *x: print(yaml.safe_dump(x, allow_unicode=True))
assert len(sys.argv) == 3
diagram = yaml.s... |
912f15fbefa955c7aaaf0d26e65102ffb18ea7ebf0ec98bf8a659c0fc191e0b1 | Python | 479 | 12 | # Copyright (c) OpenMMLab. All rights reserved.
from .backbones import * # noqa: F401, F403
from .base import BaseModel
from .builder import (build, build_backbone, build_component, build_loss,
build_model)
from .common import * # noqa: F401, F403
from .registry import BACKBONES, COMPONENTS, LOS... |
4480caf374e6c35329905c0a9fc876a53eaafdace1c32ee10a99833830f08d99 | Python | 480 | 9 | """Runs condition B of REAL_ODOURS.py in a second process (same code, same seeds -> identical counts),
writing to tmp/real_odours_counts_B.npz; merged into 06-OUTPUT/real_odours_counts.npz before `analyse`. 2026-09-25."""
import sys, runpy
sys.argv = ['REAL_ODOURS.py', 'x']
g = runpy.run_path('D:/eucys/03-CODE/REAL_ODO... |
fe0d07075c7d0b4ea8a6084bc04eea54cdde1be36b7f1c6bedd53e3173c95abf | Python | 480 | 16 | from elasticai.creator.file_generation.savable import Path
from elasticai.creator.file_generation.template import (
InProjectTemplate,
module_to_package,
)
class ENV5Constraints:
def save_to(self, destination: Path):
template = InProjectTemplate(
package=module_to_package(self.__module... |
04c9389189552e703374934bdb51c76ae690aa43f4e2b95e9baa730ba6e24e02 | Python | 481 | 18 | # Copyright (c) OpenMMLab. All rights reserved.
from mmcv.utils import collect_env as collect_base_env
from mmcv.utils import get_git_hash
import mmedit
def collect_env():
"""Collect the information of the running environments."""
env_info = collect_base_env()
env_info['MMEditing'] = f'{mmedit.__version_... |
04eee7107998c1bd11a860206be18439c375a4675d161d5a0479df50b885516e | Python | 481 | 17 | #!/usr/bin/python
# -*- coding:utf-8 -*-
import logging
def setlogger(path):
logger = logging.getLogger()
logger.setLevel(logging.INFO)
logFormatter = logging.Formatter("%(asctime)s %(message)s", "%m-%d %H:%M:%S")
fileHandler = logging.FileHandler(path)
fileHandler.setFormatter(logFormatter)
... |
06354e553e97521e60d5c8220ecdc3a5a957617594eb9dd39827a345941e60d8 | Python | 481 | 16 | from elasticai.creator.file_generation.savable import Path
from elasticai.creator.file_generation.template import (
InProjectTemplate,
module_to_package,
)
class ENV5ReconfigTop:
def save_to(self, destination: Path):
template = InProjectTemplate(
package=module_to_package(self.__module... |
4a83420c0cec2c5a4fa5c188dbc300e0d4a4fe6e91e6e9a71b03a4884769d981 | Python | 481 | 11 | def inv_lr_scheduler(optimizer, iter_num, gamma, power, lr=0.001, weight_decay=0.0005):
"""Decay learning rate by a factor of 0.1 every lr_decay_epoch epochs."""
lr = lr * (1 + gamma * iter_num) ** (-power)
i=0
for param_group in optimizer.param_groups:
param_group['lr'] = lr * param_group['lr_m... |
8acb8cbfc2f0c8cdea0ce77afa40e91593f7c2f383509e7edbe8cf2aca7fd6f0 | Python | 481 | 20 | import unittest
class TestRegexesMain(unittest.TestCase):
def test_imports(self):
try:
from dreamcoder.domains.regex.main import (
LearnedFeatureExtractor,
ConstantInstantiateVisitor,
MyJSONFeatureExtractor,
regex_options,
... |
8ff667d6a5e2831c11e1fe7360679e108ffc4a3d79502132179f42e2ceea2471 | Python | 481 | 11 | import os
import sys
group = sys.argv[1]
start = int(sys.argv[2])
stop = int(sys.argv[3])
for i in range(start, stop+1):
os.system(f"mkdir patients/{group}{i}/results32")
os.system(f"scp larswd@saga.sigma2.no:/cluster/projects/nn8017k/Lars/experiments/meshes/{group}{i}/results32/*.txt patients/{group}{i}/res... |
923f56eddf82b09970795843fdfd89a1e5630cd32c04b9471538aef27deb692a | Python | 482 | 19 | from elasticai.creator.ir import Registry
from ._ir import (
DataGraph,
pattern_rule,
)
from ._ir import (
sequential_with_interface as _sequential_with_interface,
)
def _replacement_fn(g: DataGraph, registry: Registry) -> tuple[DataGraph, Registry]:
return _sequential_with_interface(("activation", "... |
95ab0ca5ae7d88b374d7bf38c90eca0db625e2826137a23c4e58976bf8d8dfcb | Python | 482 | 12 | from .amigos import AMIGOSDataset
from .bci2022 import BCI2022Dataset
from .deap import DEAPDataset
from .dreamer import DREAMERDataset
from .mahnob import MAHNOBDataset
from .seed import SEEDDataset
from .seed_feature import SEEDFeatureDataset
from .seed_iv import SEEDIVDataset
from .seed_iv_feature import SEEDIVFeatu... |
a3c6d4ceec2a3afa9170468d50b2452c2d91e369d20450c219e8aae4802f324a | Python | 482 | 15 | """The hub's updater entry uses icon='download'; it must exist in the registry."""
from regtools.gui import icons
def test_download_glyph_registered():
assert "download" in icons.NAMES
assert "download" in icons._SVGS
def test_download_glyph_is_themable_svg():
svg = icons._SVGS["download"]
... |
fdd9d20896cc7727a1870783a1d8c1031404c682dd8f55a53ed65d98f4e3068b | Python | 482 | 18 | # Copyright (c) Open-MMLab. All rights reserved.
__version__ = '0.14.0'
def parse_version_info(version_str):
ver_info = []
for x in version_str.split('.'):
if x.isdigit():
ver_info.append(int(x))
elif x.find('rc') != -1:
patch_version = x.split('rc')
ver_in... |
f20eb628921a1cdb43ccf8684a36ffe7b7fa897016d4f1efddfdee076f9add18 | Python | 483 | 20 | from abc import ABC, abstractmethod
from elasticai.creator.file_generation.savable import Path, Savable
from elasticai.creator.vhdl.design.ports import Port
class Design(Savable, ABC):
def __init__(self, name: str):
self._name = name
@property
def name(self) -> str:
return self._name
... |
1a0872a59b931d6f1f562eca00cf9ec05f148bd48cfce42ca917ee533eda55e9 | Python | 484 | 13 | from setuptools import setup, find_packages
exec(open('plantsegtools/__version__.py').read())
setup(
name='plantsegtools',
version=__version__,
packages=find_packages(exclude=["tests"]),
include_package_data=True,
description='Suite of Python tools for plant-seg https://github.com/hci-unihd/plant-s... |
5699ff0644d5a2c4a96cde36358b9ae4634a91ac85673456a98025b6452411b9 | Python | 484 | 8 | def test_annotation_grid_matches():
from regtools.registration.point_registration import annotation_grid_matches
# in-plane (y,x) of the annotation must equal the expected (y,x)
assert annotation_grid_matches((528, 320, 456), (320, 456)) is True
assert annotation_grid_matches((512, 512, 512), (320... |
d561fc079721baa8ed6af73893998eed9f9e9af3f8eaaa12376a702d4b3a6b7a | Python | 484 | 12 | from HPRO.aodiag import AODiagKernel
kernel = AODiagKernel()
kernel.setk([[0.000000000000, 0.000000000000, 0.000000000000],
[0.333333333333, 0.333333333333, 0.000000000000],
[0.500000000000, 0.000000000000, 0.000000000000],
[0.000000000000, 0.000000000000, 0.000000000... |
3044c8e5c63a9fb6c8559b1e6fc437d327a92043a2dd83234cc9e2d3c8d5b547 | Python | 485 | 21 | """Distributional, single-time, and static discrimination metrics."""
from .distributional import concordance_time_dependent
from .single_time import auc, auc_cr, auc_cr_per_event
from .static import (
concordance,
concordance_cr,
concordance_cr_per_event,
concordance_ic,
)
__all__ = [
"auc",
... |
4c083b6fd8aaa1238336ef619907bfdae189bc633e390cc162a2c57e7823ebb9 | Python | 485 | 13 | from typing import Tuple
import thop
import torch
import torch.nn as nn
import yacs.config
def count_op(config: yacs.config.CfgNode, model: nn.Module) -> Tuple[str, str]:
data = torch.zeros((1, config.dataset.n_channels,
config.dataset.image_size, config.dataset.image_size),
... |
a9c48274c81b8c61ea540f692ad65cc86ade487fffe042df36d70d65eb91ea81 | Python | 485 | 16 | from __future__ import annotations
import pytest
pytest.importorskip("pymatgen.analysis.defects")
from pymatgen.core import Lattice, Structure
from quacc.atoms.defects import get_defect_entry_from_defect
def test_get_defect_entry_requires_dummy_site():
defect_supercell = Structure(Lattice.cubic(5), ["Si"], [[... |
91c48748d64f159b24f57f1d8225e57c764ff38501bcffaeda774967c2bd3a32 | Python | 486 | 20 | from .resnet_cifar import *
from .resnet import *
from .mobilenetv2 import mobilenet_v2_cifar
model_dict = {
"resnet18": resnet18,
"resnet34": resnet34,
"resnet50": resnet50,
"resnet101": resnet101,
"resnet152": resnet152,
"resnet20": resnet20_cifar,
"resnet32": resnet32_cifar,
"resnet... |
c5bb4068cff7287cd7ecb37adffbe996cd65bd4dd783e046072219d7d15b1c8a | Python | 486 | 20 | from setuptools import setup, find_packages
requirements = [
"numpy",
"pandas",
"xarray",
"scipy",
"msgpack<1.0",
# TODO figure out conda dev setup and add "opencv-python",
]
setup(
name="pupil_recording_interface",
version="0.5.0",
packages=find_packages(),
long_description=op... |
c721a25968280443a0c936d375cfb4e05c3cffae8d8bcc0d45070c7f7410b597 | Python | 486 | 18 | from abc import ABC, abstractmethod
import torch
from elasticai.creator.vhdl.design.design import Design
class DesignCreatorModule(ABC, torch.nn.Module):
@abstractmethod
def create_design(self, name: str) -> Design: ...
@abstractmethod
def forward(
self, inputs: torch.FloatTensor, given_inp... |
00627182df2c25eb74ed864c3139ee516acf5b7f89e8fb22e806ce6ab3fc46ec | Python | 487 | 13 | import os
import sys
ref_path = sys.argv[1]
fasta_dir_path = sys.argv[2]
sam_dir_path = sys.argv[3]
dir_list = os.listdir(fasta_dir_path)
for dir in dir_list:
test = dir.strip().split(".")
print("\nbio minimap2 -a -x map-ont --secondary=no -t 40 " + ref_path + " " + fasta_dir_path + dir + " > " + sam_dir_pa... |
a2a28c13a4f9a9465360a10d8d09ac3e9d9f33bc467887176df01d0f20fba49c | Python | 487 | 24 | # external
import pytest
# project
import textdistance
ALG = textdistance.Hamming
@pytest.mark.parametrize('left, right, expected', [
('test', 'text', 1),
('test', 'tset', 2),
('test', 'qwe', 4),
('test', 'testit', 2),
('test', 'tesst', 2),
('test', 'tet', 2),
])
def test_distance(left, rig... |
5c11a0d566efe5ebe73fbc0d4b828a50a79e8569569d446255c14fa1ecb590ff | Python | 489 | 13 | from dimension_encoding.b5k import B5kLoader, compute_noise_ceiling_b5k
from tqdm import tqdm
if __name__=="__main__":
dl = B5kLoader()
for subj_int in tqdm(range(1,5), total=4):
subject = f"CSI{subj_int}"
responses = dl.load_responses(subject)
stimdata = dl.load_stimdata(subject)
... |
525688284f35015e02032cf24301f84379903605ad408414e9ea2bbb5b06e26f | Python | 490 | 14 | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# ------------------------------------------------------------------------------
from __future__ import absolute_import
from __futu... |
ad69629f4b5409823277b05f536dd2f00f324305a02effb0c2e8e642bb33c2c6 | Python | 490 | 13 | from setuptools import setup, find_packages
setup(name='mesocircuit',
version='1.0.0',
description='Mesocircuit Model',
long_description=open('README.md').read(),
long_description_content_type='text/markdown',
url='https://github.com/INM-6/mesocircuit-model',
author='see docs/source... |
b87f788022ff933ff86d9c9fa40eec5b3bc6274c2b243d14aeafa88ca51a00f8 | Python | 490 | 12 | from HPRO.lcaodiag import LCAODiagKernel
kernel = LCAODiagKernel()
kernel.setk([[0.000000000000, 0.000000000000, 0.000000000000],
[0.333333333333, 0.333333333333, 0.000000000000],
[0.500000000000, 0.000000000000, 0.000000000000],
[0.000000000000, 0.000000000000, 0.000... |
1a8c0380aba4773b29be1d769ca841956c8d123a21252b6400b7627c7d227dea | Python | 491 | 25 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
import common_setup
REVS = ["issue540-base", "issue540-v1"]
LIMITS = {"search_time": 300}
SUITE = suites.suite_optimal_with_ipc11()
CONFIGS = {
"blind": ["--search", "astar(blind())"],
"ipdb": ["--search", "astar(ipdb(max_time=150))"... |
373f3462501eb35f3c078281afa3e05ed2fd5173834dd3fb82391d37c36f32bb | Python | 491 | 24 | # external
import pytest
# project
import textdistance
ALG = textdistance.Levenshtein
@pytest.mark.parametrize('left, right, expected', [
('test', 'text', 1),
('test', 'tset', 2),
('test', 'qwe', 4),
('test', 'testit', 2),
('test', 'tesst', 1),
('test', 'tet', 1),
])
def test_distance(left,... |
39412a515849035f93161236809a667bed32d8f66ae1b3a95928a5df69ad5e2a | Python | 491 | 24 | #!/usr/bin/python
# -*- coding:utf-8 -*-
from datasets.CWRU import CWRU
from datasets.CWRUFFT import CWRUFFT
from datasets.PU import PU
from datasets.PUR import PUR
from datasets.PUFFT import PUFFT
from datasets.PU_type import PU_type
from datasets.PUFFT_type import PUFFT_type
from datasets.JNU import JNU
from dat... |
3bbd58ebf67ef37947e9dbdc44dfe847d74f02c78db45c0a1ca11fe51bf4b266 | Python | 491 | 13 | from .dataprocessor import AutosomeDataProcessor
from .first_layers_block import AutosomeFirstLayersBlock
from .coreblock import AutosomeCoreBlock
from .final_layers_block import AutosomeFinalLayersBlock
from .predictor import AutosomePredictor
from .trainer import AutosomeTrainer
__all__ = ("AutosomeDataProcessor",
... |
42bb8d3b355e066e337c02f1963ae550857b775294900a233296c1d6c692b6df | Python | 491 | 28 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Documentation:
Tests for hybridLFPy classes and methods
"""
def _test(verbosity=2):
'''
Run unittests for hybridLFPy
Arguments
---------
verbosity : int
verbosity level
Returns
-------
None
'''
import unittest
f... |
619cecc7a04818973e5c285cb28a1e38dc610e316abf830040f669b30efc8989 | Python | 491 | 22 | """
Registration backend subpackage.
Provides ANTs SyN and Elastix B-spline registration backends, plus a
unified method-agnostic interface.
"""
from .ants import (
antsRegistration,
antsTransformation,
exportAntsDeformationField,
antsTransformPoints,
build_fixed_mask_from_annotation,
... |
741825f3d7bf62b3ac9a732c69505ebaf42b722933d884cbed9c0c9a92737575 | Python | 491 | 35 | from re import L
import numpy as np
from topaz.model.features.basic import BasicConv, Conv63, Conv127
class TestBasicConv():
def test_init(self):
layers = [5]
units = 5
model = BasicConv(layers, units)
def test_fill(self):
pass
def test_unfill(self):
pass
... |
9027a19b2d146816bda15303ed9219ae7b307e73f72d767996f9cd2402f92413 | Python | 491 | 23 | """Wrappers to call pyproject.toml-based build backend hooks.
"""
from ._impl import (
BackendInvalid,
BackendUnavailable,
BuildBackendHookCaller,
HookMissing,
UnsupportedOperation,
default_subprocess_runner,
quiet_subprocess_runner,
)
__version__ = '1.0.0'
__all__ = [
'BackendUnavaila... |
d31ce86a919e2f643d3cc42841d6c4929b82ad028c4962aca1ec8e285ec13aac | Python | 491 | 24 | # built-in
from math import isclose
# external
import pytest
# project
import textdistance
ALG = textdistance.Overlap
@pytest.mark.parametrize('left, right, expected', [
('test', 'text', 3.0 / 4),
('testme', 'textthis', 4.0 / 6),
('nelson', 'neilsen', 5.0 / 6),
])
def test_distance(left, right, expect... |
16bc4cc35863bf7f061ab7d5c8e89d72eea499a1f0c5dab59375da532ec71576 | Python | 492 | 25 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
import common_setup
REVS = ["issue511-base", "issue511-v1"]
LIMITS = {"search_time": 1800}
SUITE = suites.suite_optimal_with_ipc11()
CONFIGS = {
"astar_blind": ["--search", "astar(blind())"],
"astar_hmax": ["--search", "astar(hmax())... |
2fb0a8a2292331735117d312b217f8172cc60b0d94dfe3a0ad417770ba7d77a5 | Python | 492 | 25 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
import common_setup
REVS = ["issue456-base", "issue456-v2"]
LIMITS = {"search_time": 1800}
SUITE = suites.suite_optimal_with_ipc11()
CONFIGS = {
"astar_blind": ["--search", "astar(blind())"],
"astar_hmax": ["--search", "astar(hmax())... |
86b9c6ae74ae6ba3f573e41ded8e5eec5d171d15e22575cc24847ead560dcdef | Python | 492 | 25 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
import common_setup
REVS = ["issue456-base", "issue456-v1"]
LIMITS = {"search_time": 1800}
SUITE = suites.suite_optimal_with_ipc11()
CONFIGS = {
"astar_blind": ["--search", "astar(blind())"],
"astar_hmax": ["--search", "astar(hmax())... |
2c723d05a4f25d89cb20fc5fc4a35749d128dcba9ca49dc0b9746d6ecc8ca85f | Python | 493 | 23 | import numpy as np
import scipy.stats as st
def toRanks(A):
AA = np.zeros_like(A)
for i in range(A.shape[1]):
AA[:, i] = st.rankdata(A[:, i])
AA = np.array(np.around(AA), dtype="int") - 1
return AA
def gaussianize(Y):
N, P = Y.shape
YY = toRanks(Y)
quantiles = (np.arange(N) + 0.... |
94b3c7522e2e0f10b7b60a43d763d27e229b847ad86a3c1979bb2ab8cfa55ec9 | Python | 493 | 13 | from elasticai.creator_plugins.combinatorial.wiring import connect_data_signals
def test_connecting_explicit_data_signals():
edge = ("src", "dst", ((1, 1),))
code = tuple(connect_data_signals((edge,)))
assert code == ("d_in_dst(1) <= d_out_src(1);",)
def test_connecting_data_signals_by_range():
edge... |
b47dd5f15d0d246edad9b1308bad465517d1911f14403bed2373a8b317a6cacf | Python | 495 | 19 | from numpy.random import seed
from wonambi.utils import create_data
from wonambi.trans import concatenate
seed(0)
data = create_data(n_trial=5)
def test_concatenate_trial():
data1 = concatenate(data, axis='trial')
assert data1.number_of('trial') == 1
assert data1.list_of_axes == ('chan', 'time', 'trial... |
d5878fb71dfa4ca7266907855fe0cb24fbd35502dbba0d98cfba709cd336b18d | Python | 495 | 21 | import unittest
class TestEcModule(unittest.TestCase):
def test_imports(self):
try:
from dreamcoder.dreamcoder import (
commandlineArguments,
ECResult,
ecIterator,
evaluateOnTestingTasks,
default_wake_generative,
... |
4c1d414cfdfd51107695ba5b565ccd4b4b1dcdabb4ca6731337d63c089f0f488 | Python | 496 | 17 | import pandas as pd
class UniqueAggregator:
def __init__(self, column):
self.column = column
def execute(self, adata):
# column can be cell index or bin number
if self.column == "index":
values = adata.obs.index
else:
values = adata.obs[self.column].uni... |
9c7c3568e1859942a81911544b6303f7b1b7b6ab95f30c52aa4797c40d616554 | Python | 496 | 16 | # Auto-generated by nodebpy.assets.dump_library — do not edit manually.
from .ambient_occlusion_internal import AmbientOcclusionInternal
from .color_ao import ColorAO
from .flat_internal import FlatInternal
from .mn_color import MNColor
from .outline_mask import OutlineMask
from .transparent_outline_internal import Tra... |
d92e2d9170e29ef421bb819c0f300c1db90ac3efd5447f95f8db1a4bf44473d9 | Python | 496 | 16 | from typing import Tuple
import torch
import torch.nn as nn
class MixupLoss:
def __init__(self, reduction: str):
self.loss_func = nn.CrossEntropyLoss(reduction=reduction)
def __call__(
self, predictions: torch.Tensor,
targets: Tuple[torch.Tensor, torch.Tensor, float]) -> torc... |
1c1fbe1388fe8e876a100b5e934763e62e49e69ea5df1e28be421567b5dfd192 | Python | 497 | 16 | from typing import Tuple
import torch
import torch.nn as nn
class CutMixLoss:
def __init__(self, reduction: str):
self.criterion = nn.CrossEntropyLoss(reduction=reduction)
def __call__(
self, predictions: torch.Tensor,
targets: Tuple[torch.Tensor, torch.Tensor, float]) -> tor... |
382eb87214218e0920b54c99ec3105e7e035420a38c9c3860ff5e358b8cea129 | Python | 497 | 14 | # There is some weird behaviour with importing the utils module here vs that in the datasets module.
# Relative imports are fine but to make the base utils module available at top level we have to
# import it before the datasets module, and give it another name.
from . import utils as core_utils
from .cluster import *... |
ac5080be0ae2defb658a2861d3a95bd5b790b25421b390f7d6573bb9f035c66a | Python | 497 | 19 | # By Shuran Liu, 2023
import torch
import torch.nn.functional as F
class lossfun(torch.nn.Module):
def __init__(self):
super(lossfun, self).__init__()
def forward(self, gt, oups, bayesian=False):
if bayesian:
mean = oups[:,:,0:1,:,:]
std = oups[:,:,1:2,:,:]
... |
bd4cbbfc2a3c54014e953e00f68f609856be8a91c966920786487b18491bfe66 | Python | 497 | 13 | from .aliases import Alias, Aliases
from .basemodel import BaseModel
from .claims import Claim, Claims
from .descriptions import Descriptions
from .forms import Form, Forms
from .labels import Labels
from .language_values import LanguageValue, LanguageValues
from .lemmas import Lemmas
from .qualifiers import Qu... |
26327f718470636c684bf26395c27ea9f4c56ec41d3ef5faca18c781c2217924 | Python | 498 | 24 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
import common_setup
REVS = ["issue554-base", "issue554-v1"]
LIMITS = {"search_time": 1800}
SUITE = suites.suite_optimal_with_ipc11()
CONFIGS = {
"astar_hmax": ["--search", "astar(hmax())"],
"gbfs_gc": ["--search", "eager_greedy(goalc... |
586d8f48ad90ac7368c21c0b1440fb8469a87222abd3c7d3763bd739d884dd03 | Python | 498 | 20 | import numpy as np
import syncopy as spy
from syncopy import synthdata
cfg = spy.StructDict()
cfg.nTrials = 50
cfg.nSamples = 2000
cfg.samplerate = 250
# 3x3 Adjacency matrix to define coupling
AdjMat = np.zeros((3, 3))
# only coupling 0 -> 1
AdjMat[0, 1] = 0.2
data = synthdata.ar2_network(AdjMat, cfg=cfg, seed=42)
... |
c18c6939f1a472fc1747f1fd0bc4e6df4a2b86c6f0d4013f659573430d2f5277 | Python | 498 | 24 | import numpy as np
import traj_dist.distance as tdist
import pickle
traj_list = pickle.load(open("/Users/bguillouet/These/trajectory_distance/data/benchmark_trajectories.pkl", "rb"))[:10]
traj_A = traj_list[0]
traj_B = traj_list[1]
# Simple distance
dist = tdist.sspd(traj_A, traj_B)
print(dist)
# Pairwise distanc... |
f350dac8d2469e39ef446b9685e1830a3a81cd621038e03debb4087db801657d | Python | 499 | 15 | """The launcher writes a crash log to the project root on bootstrap failure."""
import os
def test_write_crash_log_creates_file(tmp_path, monkeypatch):
from regtools.updater import launch
monkeypatch.setattr(launch, "get_project_root", lambda: str(tmp_path))
try:
raise ValueError("boom... |
91266222f5913c79cd844ee9c3b1ab87c2fa09b9c1760e6ccf9a9e207f71d700 | Python | 500 | 16 | import umap
def fit_trafo_umap(x_train, x_test, umap_params):
fit_params ={"n_neighbors": 100,
"min_dist": 0.5,
"n_components": umap_params["n_components"],
"metric":'euclidean',
"n_jobs": 10}
dim_red = umap.UMAP(**fit_params)
trafo ... |
92c776522fb902ce0b9e8319c67577c0310bdc47ef18eb2dc3448fc57a671b63 | Python | 500 | 13 | import numpy as np
from regtools.registration.run_provenance import region_voxel_volumes
def test_region_voxel_volumes():
ann = np.array([[1, 1, 2], [2, 2, 0]], dtype=np.int32) # region1: 2 vox, region2: 3 vox
v = region_voxel_volumes(ann, 2.0) # 0.025³ etc. in practice
assert v[1] == 4.0 and v[2... |
d0225644200dcef395d10a62190ee348565361a238208cb246a127091970da92 | Python | 500 | 10 | import importlib
from basicsr.utils import scandir
from os import path as osp
# automatically scan and import arch modules for registry
# scan all the files that end with '_arch.py' under the archs folder
arch_folder = osp.dirname(osp.abspath(__file__))
arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scand... |
58e09007731c8ff5b76939bcc0b8e58f7b63333055d262179fb6700bea19f2b3 | Python | 501 | 21 | import unittest
class TestTextMain(unittest.TestCase):
def test_imports(self):
try:
from dreamcoder.domains.text.main import (
ConstantInstantiateVisitor,
LearnedFeatureExtractor,
competeOnOneTask,
sygusCompetition,
... |
9185594a65d3e825889d3e1424f0edad2401019bbc7ccb85071a6fe46b034cb9 | Python | 501 | 15 | # This file is dual licensed under the terms of the Apache License, Version
# 2.0, and the BSD License. See the LICENSE file in the root of this repository
# for complete details.
__title__ = "packaging"
__summary__ = "Core utilities for Python packages"
__uri__ = "https://github.com/pypa/packaging"
__version__ = "23... |
953ad1b4842acdff9b913cbfa461fc70622971f9875f1318241a4a678f3e30ab | Python | 501 | 14 | from .k_fold import *
from .k_fold_cross_subject import *
from .k_fold_cross_trial import *
from .k_fold_groupby_trial import *
from .k_fold_per_subject import *
from .k_fold_per_subject_cross_trial import *
from .k_fold_per_subject_groupby_trial import *
from .leave_one_subject_out import *
from .split import *
from .... |
c9303d4514c8b09f2ccc87d1048fd4b408f46b253198d3dc46443d9b53cda5bd | Python | 502 | 29 | from .config import (
MappingConfig,
)
from .selection import (
select_cluster_prototypes,
select_high_confidence_examples,
select_boundary_examples,
)
from .rendering import (
render_selection_montage,
)
from .stage import (
MappingStage,
MappingResult,
)
__all__ = [
"MappingConfig... |
c071733eee333911c1737129eb4b361fb9681bf15ec2e0f5db64eebe704fa320 | Python | 503 | 20 | """``pyflagser`` is a python API for the C++ flagser library."""
# PEP0440 compatible formatted version, see:
# https://www.python.org/dev/peps/pep-0440/
#
# Generic release markers:
# X.Y
# X.Y.Z # For bugfix releases
#
# Admissible pre-release markers:
# X.YaN # Alpha release
# X.YbN # Beta release
# X.YrcN # Releas... |
0e040629a6fc83cc4ead0497d96825f126d700a4a714ad3abcd16ab037662758 | Python | 506 | 11 | """Example B: a dockable panel. Qt is imported only inside the factory, so headless use never needs it."""
def create_panel(context):
from .panel import TelemetryPanel # imported on the GUI thread, only when the panel is shown
return TelemetryPanel(context)
def register(plugin):
plugin.panel("example_t... |
37f94d2a045342588e1c451e5e5ee4763d3ea2ce88189733c3d3abe8edb87615 | Python | 506 | 25 | import numpy as np
import pandas as pd
from topaz.utils.conversions import (boxes_to_coordinates,
coordinates_to_boxes,
coordinates_to_eman2_json,
coordinates_to_star, mirror_y_axis)
def test_boxes_to_coordin... |
7af3e6d7690b818a939bea5bce6eb46cebae9ae993f08a41356169d2e332af31 | Python | 506 | 36 | """Read resources contained within a package."""
from ._common import (
as_file,
files,
Package,
)
from ._legacy import (
contents,
open_binary,
read_binary,
open_text,
read_text,
is_resource,
path,
Resource,
)
from .abc import ResourceReader
__all__ = [
'Package',
... |
e2ed0e736acc49ae04ff317e30f3f3c536a62a6961992db595cd43f7199c66c7 | Python | 506 | 13 | from .model import Model
from .basecall import basecall
from .modeltinynoskipx4 import ModelTinyNoSkipX4
from .modeltinynoskipx1 import ModelTinyNoSkipX1
from .modeltinynoskipx0111 import ModelTinyNoSkipX0111
from .modeltinynoskipx011 import ModelTinyNoSkipX011
from .modeltinynoskipx01 import ModelTinyNoSkipX01
from .m... |
3c1bb6cd764421307051182e3d4dc61da856747238d04751275303cf0cb26147 | Python | 507 | 23 | import numpy as np
import matplotlib.pyplot as plt
def history_plot(history: dict, figname: str, show: bool = False):
# make plot
plt.figure(1, figsize=(10, 8))
n = np.ceil(np.sqrt(len(history))).astype(int)
for i, (k, v) in enumerate(history.items()):
plt.subplot(n, n, i + 1)
plt.pl... |
50083d11a5c9710f9b7dbd4ca012f8e3721fd0c78f63a36a6a0a1a0d82a13ede | Python | 507 | 13 | # -*- coding: utf-8 -*-
# File : __init__.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
from .batchnorm import SynchronizedBatchNorm1... |
b47e81b0629f76d71f9927e3bab83c179b5c2d32d0a6d7ae548722d14455bc2d | Python | 507 | 17 | import optuna
from optuna_dashboard import run_server
def objective(trial):
x = trial.suggest_float("x", -100, 100)
y = trial.suggest_categorical("y", [-1, 0, 1])
return x**2 + y
if __name__ == "__main__":
storage = optuna.storages.InMemoryStorage()
study = optuna.create_study(storage=storage, s... |
e92dee6a1efb3c981bf424dbce646844a5b7abccfd61591c5f974c2f6a0fc777 | Python | 507 | 13 | from .dataprocessor import UnlockDNADataProcessor
from .first_layers_block import UnlockDNAFirstLayersBlock
from .coreblock import UnlockDNACoreBlock
from .final_layers_block import UnlockDNAFinalLayersBlock
from .predictor import UnlockDNAPredictor
from .trainer import UnlockDNATrainer
__all__ = ("UnlockDNADataProces... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.