sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a44ad664b7ad4e59aca7da761c83b6c7829b4f24fa6c0723ab110a532c35e117 | Python | 308 | 12 | from setuptools import setup
setup(
name="CEC2017",
version="1.0",
description="CEC 2017 single objective optimization suite",
author="Duncan Tilley",
url="https://github.com/tilleyd/cec2017-py",
packages=["cec2017"],
include_package_data=True,
install_requires=["numpy"],
)
|
b656aee9ae688409db3eb185392537cbad829004ca5ed4e0fc4173bcc46ccd1e | Python | 308 | 20 | #
# A very simple test of zee-calibrator.
#
from PyQt5 import QtCore
import time
import pytestqt
import storm_control.zee_calibrator.main as zcal
def _test_zeecal_starts(qtbot):
mainw = zcal.Window()
mainw.show()
qtbot.addWidget(mainw)
# Run for about 0.5 seconds.
qtbot.wait(500)
|
f50434744063723ee4c0794c36768c3d90e5ad4fe1feb2bd710e738555252cba | Python | 308 | 12 | """Backend modules implementing applications and widgets."""
from __future__ import annotations
BACKENDS: dict[str, tuple[str, str]] = {
"Qt": ("_qtpy", "qtpy"),
"ipynb": ("_ipynb", "ipynb"),
}
for key in list(BACKENDS):
if not key.islower():
BACKENDS[key.lower()] = BACKENDS.pop(key)
|
32328701ab5c3e764f20e1ddb6434eaab615cc63ee9117d3c616f83bdf1c4010 | Python | 309 | 7 | from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from brainscore_vision import model_registry
from .model import get_layers,get_model
model_registry['grcnn'] = \
lambda: ModelCommitment(identifier='grcnn', activations_model=get_model('grcnn'), layers=get_layers('grcnn')) |
572f001cdb10f7375513b7c8601fd343e98d3a407ffeded0df9b38299cd42639 | Python | 309 | 8 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, LAYERS
model_registry['alexnet_ks_torevert'] = lambda: ModelCommitment(
identifier='alexnet',
activations_model=get_model(),
layers=LAYERS)
|
69fdfa045e95ec78ed02b8ea87df35759c7b487ec2f22b16f0fc1f981301dea6 | Python | 309 | 17 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author: Yong Bai, yong.bai@hotmail.com
@License: (C) Copyright 2013-2022.
@File: __init__.py.py
@Desc:
"""
from ._svgplot import svgplot
# from ._setting import set_global_params, set_fontsize_dpi
from ._venn import *
from ._heatmapplot import gheatmap
|
6b5d28059919df4a0a791fa84464feccdeabda712ffa7c72ccd5b1633dcbed49 | Python | 312 | 13 | import glob
# EDIT SCRIPT TO HAVE RIGHT FILES
outFile = open('Tasha_CanFam4.repeatmasker.cmds','w')
fl = glob.glob('chunk-segments/*fa')
print('have %i fl' % len(fl))
for f in fl:
# EDIT to have write species
cmd = 'RepeatMasker -species dog -e ncbi ' + f + '\n'
outFile.write(cmd)
outFile.close() |
c483a0d7efd63fb880582c5310663c0da63bc233ddf3877663bbfe4fc33b3f67 | Python | 312 | 13 | import torch
from torch import nn
def accuracy(output, labels):
preds = output.max(1)[1].type_as(labels)
correct = preds.eq(labels).double()
correct = correct.sum()
return correct / len(labels)
def init_weights(m):
if type(m)==nn.Linear:
nn.init.kaiming_uniform_(m.weight)
|
50ecf68c5307643a04d58faf543022175f78a020ea171e39740b180c5d4301b7 | Python | 313 | 21 |
INFO = {
"dtiplayground": {
"version" : "0.8.1"
},
"dmriplayground": {
"version" : "0.8.1"
},
"dmriprep": {
"version" : "0.8.1"
},
"dmriatlas": {
"version" : "0.2.0b1"
},
"dmriautotract" : {
"version" : "0.0.2b1"
},
"dmrifiberprofile" : {
"version" : "0.8.1"
}
}
|
63eb10fe25b1e950ff165af6e0b058386f048928275ed78b38a5b9fafe5032cb | Python | 313 | 8 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, LAYERS
model_registry['alexnet_testing'] = lambda: ModelCommitment(
identifier='alexnet_testing',
activations_model=get_model(),
layers=LAYERS)
|
afbbd945f31227f4426e13d877e97516d627de3c36bfa33ddcbfd8b97d63e1a3 | Python | 313 | 8 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, LAYERS
model_registry['AlexNet_SIN'] = lambda: ModelCommitment(
identifier='AlexNet_SIN',
activations_model=get_model(),
layers=LAYERS)
|
b306b35b00151e9994e31c1c3a48846d1b4f33538889162d642873c5c9cc303c | Python | 313 | 8 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, LAYERS
model_registry['alexnet_1_21_26'] = lambda: ModelCommitment(
identifier='alexnet_1_21_26',
activations_model=get_model(),
layers=LAYERS)
|
35e7165ce689163709008b42d90c01f264333fb86b62d55e4dc5cd18ebe439c6 | Python | 314 | 9 | # Created by David Coggan on 2024 06 25
from brainscore_vision import benchmark_registry
from .benchmark import (
Coggan2024_behavior_ConditionWiseAccuracySimilarity)
benchmark_registry['tong.Coggan2024_behavior-ConditionWiseAccuracySimilarity'] = (
Coggan2024_behavior_ConditionWiseAccuracySimilarity)
|
397669f5659dc98690d85994820b98bec68a7fcf7680b1cdf50d1f058b022090 | Python | 314 | 12 | import json
import numpy as np
from nlp_features import *
file='train'
name='/data2/whr/lyh/project3/Twibot-20/'+file +'.json'
data=np.load('Twibot-20/node_fea.npy')
with open(name,'r') as f:
users=json.load(f)
for user in users:
tweets=user['tweet']
sent=sentiment(tweets)
print()
|
477dc18a31cbdadcb7f3c866604e913e03ab5ecec898811a40056d4ded754dc8 | Python | 314 | 9 | """Nottingham MEGUK: Save Training Data for Dynamic Network Analysis."""
from glob import glob
from osl_dynamics.data import Data
outdir = "data/preproc"
files = sorted(glob(f"{outdir}/*/sflip-lcmv-parc-raw.fif"))
data = Data(files, picks="misc", reject_by_annotation="omit")
data.save("training_data/networks")
|
614a860a9524e2100092cb0d92bed68c785d662d1316a4a9f66e1095f6f3820d | Python | 314 | 11 | #!/usr/bin/env python
import gzip
import pandas as pd
import sys
cell_line = sys.argv[1]
region = sys.argv[2]
training_df = pd.read_hdf('{}/output-{}/training.h5'.format(cell_line, region), 'training')
training_df.to_csv(gzip.open('{}/output-{}/training.csv.gz'.format(cell_line, region), 'wt'), index = False)
|
c0ab54956d3c4cbd148ec6d28bde395b4d65bc5eff74adbd4faddde75acb27ea | Python | 314 | 23 |
class DMRIXInput(object): ## standard data
def __init__(*args,**kwargs):
self.inputs = []
pass
class DMRIPlayground(object):
def __init__(*args,**kwargs):
pass
def initialize(self):
pass
def preprocess(self):
pass
def process(self):
pass
def postprocess(self):
pass |
dac9cb1ef341b93f25ce43ef128e8b6a4c7839c2c4173a210430a0e80f776625 | Python | 314 | 15 | import numpy as np
from sklearn.linear_model import LinearRegression
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
# y = 1 * x_0 + 2 * x_1 + 3
y = np.dot(X, np.array([1, 2])) + 3
reg = LinearRegression().fit(X, y)
reg.score(X, y)
print(reg.coef_)
print(reg.intercept_)
print(reg.predict(np.array([[3, 5]])))
|
0ec42f6c98f177f95bf8809d33668e4591f41bf6f32f46738a1279615156785a | Python | 315 | 14 | from ._version import get_versions
__version__ = get_versions()['version']
del get_versions
from .log_cfg import logger, unconfigure_logging, enable_logging
__author__ = ', '.join([
'David Sebastian Fischer',
'Florian Hölzlwimmer'
])
__email__ = ', '.join([
'david.fischer@helmholtz-muenchen.de'
])
|
8194609200795387a3ed306603fc5088df3b2b19325148162c2464afe54ccffc | Python | 315 | 8 | from importlib.metadata import PackageNotFoundError, version
try:
__version__ = version("ccf_streamlines")
except PackageNotFoundError:
# package is not installed; use a PEP 440-parseable sentinel so consumers
# calling packaging.version.parse(__version__) do not blow up
__version__ = "0+unknown"
|
942b99428deeb69dae35851c1f4f2f50d7d190faa3844015ac1c6eb186b7fe78 | Python | 315 | 15 | from typing import List
import pytest
import sh
def run_command(command: List[str]):
"""Default method for executing shell commands with pytest."""
msg = None
try:
sh.python(command)
except sh.ErrorReturnCode as e:
msg = e.stderr.decode()
if msg:
pytest.fail(msg=msg)
|
b321768c9143cfa9a938b23dac15e6fd45ace1d5556e2dd9bec20c174faffa8f | Python | 315 | 15 | """# Range slider widget
A double ended range slider widget.
"""
from magicgui import magicgui
@magicgui(auto_call=True, range_value={"widget_type": "RangeSlider", "max": 500})
def func(range_value: tuple[int, int] = (20, 380)):
"""Double ended range slider."""
print(range_value)
func.show(run=True)
|
c8d13bf2ffb724d24e1c5dc97355c70472de9fdef9d0a005c825f39f21583385 | Python | 315 | 14 | """Utility modules for DHG-LGB framework."""
from .logger import setup_logger, get_logger
from .io import load_config, save_pickle, load_pickle, save_numpy, load_numpy
__all__ = [
'setup_logger',
'get_logger',
'load_config',
'save_pickle',
'load_pickle',
'save_numpy',
'load_numpy',
]
|
f1c3671078d782fd5ff0f41fa792c27b3a679d7107b8a881fb94713a22ebc7e9 | Python | 315 | 2 | #from func import *
from func_ELA import ELA_MEM_check_accuracy, ELA_MEM_LM, ELA_MEM_plot_Energy_Ranking, ELA_Network_calc_E, ELA_Network_calc_majorSS, ELA_Network_calc_RA, ELA_Network_calc_SS, ELA_Network_calc_TP_onlySS, ELA_Network_calc_TP, ELA_Network_functions, ELA_Network_plot_DG, ELA_Network_plot_Graph_pyvis |
51687661409c6d40333205f23f7dcec0251874d32730201e2cd6faafd15c6bb6 | Python | 316 | 9 | # initilize all models, support dynamic import
import os
import importlib
modules = os.listdir(os.path.join(os.path.abspath('.'),'models'))
modules = [os.path.splitext(module)[0] for module in modules if '__' not in module]
for module in modules:
importlib.import_module('.' + module, package=__name__) |
0ea5a5a6c47c576f73f6dd07b98b11e140d083f518198c1accebf988d96ede09 | Python | 317 | 17 | # Generated by Django 3.1.1 on 2020-10-19 11:00
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
("dataset_repo", "0004_rename"),
]
operations = [
migrations.AlterModelTable(
name="gitdata",
table=None,
),
]
|
b3f7816b35d7a3511632287ff09c976d439fda57b2e1323f996c826d50705629 | Python | 318 | 14 | #!/usr/bin/env python
from storm_control.test.hal.standardHalTest import halTest
def test_hal_tcp_nsm1():
halTest(config_xml = "none_tcp_config.xml",
class_name = "NoSuchMessage1",
test_module = "storm_control.test.hal.tcp_tests")
if (__name__ == "__main__"):
test_hal_tcp_nsm1()
|
f2f215686639181b4ee9a3f822e01f47fa9aac79193c92ed4b53537436d8eed9 | Python | 319 | 16 | """
nelpy.examples
==============
This module contains helper functions to load example data.
"""
from ._utils import (
download_example_dataset,
get_example_data_home,
load_example_dataset,
)
__all__ = ["load_example_dataset", "download_example_dataset", "get_example_data_home"]
__version__ = "0.0.1"
|
2213cdd46ed2484f22bfe2d5e2ee407692fb1f8c109b02fe54900d3711a26b7e | Python | 320 | 5 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['grcnn_109'] = lambda: ModelCommitment(identifier='grcnn_109', activations_model=get_model('grcnn_109'), layers=get_layers('grcnn_109'))
|
5ee1166c536486642d50a091a3a157953e1ad67ce199ae461435cc43bd0d6b02 | Python | 320 | 16 | from torch.autograd import Function
class ReverseLayerF(Function):
@staticmethod
def forward(ctx, x, alpha):
ctx.alpha = alpha
return x.view_as(x)
@staticmethod
def backward(ctx, grad_output):
output = grad_output.neg() * ctx.alpha
return output, None |
8b6868c492154aa7bdeacb8f4d429a7ee216ff20d6af52c805941498aa3bf64f | Python | 320 | 14 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
# pylint: disable=unused-import
from cvat_sdk.api_client.exceptions import (
ApiAttributeError,
ApiException,
ApiKeyError,
ApiTypeError,
ApiValueError,
OpenApiException,
)
from cvat_sdk.core.exceptions import CvatSdkException
|
ccbcdea5bbef9698e145bb3723af500e22d0fea484403ddd060421a991636e16 | Python | 320 | 9 | import unittest
import base
class Test(base.BaseScriptTest, unittest.TestCase):
command_line = "./scripts/lzop_build_offset_table.py"
input_stdin = base.TestFile(filename="./test_data/maf_tests/mm8_chr7_tiny.maf.lzo")
output_stdout = base.TestFile(filename="./test_data/maf_tests/mm8_chr7_tiny.maf.lzot")
|
23ba133ab1b84b9c989f0a57c2a54204d541eb2d31877c52f3ba4d344d8b2fa2 | Python | 321 | 10 | import sys
from qtpy.QtWidgets import QApplication
from probe_vis.view.ProbeVisualizer import ProbeVisualizer
# sys.path.append("C:\\Users\\xiao\\GitHub\\probe4Xiao\\probe_vis")
if __name__ == "__main__":
app = QApplication(sys.argv)
probeV = ProbeVisualizer()
probeV.show()
sys.exit(app.exec())... |
513f7b0699dab75d621b3e0bb91c177c417415129e1fee9fa64d9a632a17d603 | Python | 321 | 15 | from dataclasses import dataclass
from eir.data_load.label_setup import al_label_transformers
from eir.setup.schemas import InputConfig
@dataclass
class ComputedServeTabularInputInfo:
labels: "ServeLabels"
input_config: InputConfig
@dataclass
class ServeLabels:
label_transformers: al_label_transformers... |
5b485510a188d8e548a4d5e2e8a1a4bd2fd2c495c27f39ed2900267c3af68a65 | Python | 321 | 11 | import cv2
import numpy as np
def read_image(path):
img = cv2.imread(path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # opencv default color space is BGR, change it to RGB
p = np.percentile(img, 90)
q = np.percentile(img, 3)
img = np.clip(img * 255.0 / p, 0, 255).astype(np.uint8)
return img, q... |
1c743dcb051fc115f0dc3b8811b463db5d965899084bf3b21340f38fdb0919b9 | Python | 322 | 6 | from brainscore_vision import benchmark_registry
from .benchmark import Hermann2020cueconflictShapeBias, Hermann2020cueconflictShapeMatch
benchmark_registry['Hermann2020cueconflict-shape_bias'] = Hermann2020cueconflictShapeBias
benchmark_registry['Hermann2020cueconflict-shape_match'] = Hermann2020cueconflictShapeMatc... |
b498568c02b52e62dfaf3cb5eb3fc1c8d945e52a53e56ba6f2efecc7c794ad19 | Python | 322 | 10 | from pytest import approx
from brainscore_vision import load_metric, load_dataset
def test_identity_circularvariance():
assembly = load_dataset('Ringach2002')
metric = load_metric('ks_similarity', property_name='circular_variance')
score = metric(assembly, assembly)
assert score == approx(1, abs=0.05... |
b6fb114e45e41a83be76b0a1f75fd79ef3945bf3fefb94ea5310a91fb4f80be2 | Python | 323 | 5 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['model-lecs'] = lambda: ModelCommitment(identifier='model-lecs', activations_model=get_model('model-lecs'), layers=get_layers('model-lecs... |
bdc318e662b5b43bcb65a6ec98378a205aae02d23b3e88b33034fa8bbf1f45dd | Python | 323 | 10 | import sys
from PyQt5.QtWidgets import QApplication
from probe_vis.view.ProbeVisualizer import ProbeVisualizer
# sys.path.append("C:\\Users\\xiao\\GitHub\\probe4Xiao\\probe_vis")
if __name__ == "__main__":
app = QApplication(sys.argv)
probeV = ProbeVisualizer()
probeV.show()
sys.exit(app.exec_... |
cb32fc435029d9eb457af73960fb0fbf9ef30c55e57513345f6bd18c0ac53fb1 | Python | 323 | 7 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from .predict import SemanticSegmentationPredictor
from .train import SemanticSegmentationTrainer
from .val import SemanticSegmentationValidator
__all__ = "SemanticSegmentationPredictor", "SemanticSegmentationTrainer", "SemanticSegmentationValidator"... |
f3fcce9491c522f75116ca4067561e160268566fcf4a28dd70cab9f8c49023f6 | Python | 323 | 15 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from django.apps import AppConfig
from health_check.plugins import plugin_dir
class HealthConfig(AppConfig):
name = "cvat.apps.health"
def ready(self):
from .backends import OPAHealthCheck
plugin_dir.register(OPAHealthChec... |
5d5edf8d58303bb501e9ad2096cfc0bf77a52a2a53dda2e14454d7bb397de7f5 | Python | 324 | 15 | from importlib.metadata import version
__version__ = version("napari-debcr")
from ._reader import get_reader
from ._writer import npz_file_writer
#from ._sample_data import make_sample_data
from ._plugin import DeBCRPlugin
__all__ = (
"get_reader",
"npz_file_writer",
# "make_sample_data",
"DeBCRPlugin... |
f158f92243601e2abb49af164c782c9df30ac0a01e042c576be7bd166c7d2c79 | Python | 324 | 10 | from tests.utils import pytest_importorskip
def test_qtscxml():
"""Test the qtpy.QtScxml namespace"""
QtScxml = pytest_importorskip("qtpy.QtScxml")
assert QtScxml.QScxmlCompiler is not None
assert QtScxml.QScxmlDynamicScxmlServiceFactory is not None
assert QtScxml.QScxmlExecutableContent is not N... |
66ed72199709b7f8f899f0c077d10650b0715fb95a6dd49d4aa47dcca95b8d28 | Python | 325 | 12 | color_scheme = {
'concrete-baseline': "#ffd700",
'concrete-subsective': "#ffb14e",
'concrete-privative': "#fa8775",
'abstract-baseline': "#cd34b5",
'abstract-subsective': "#9d02d7",
'abstract-privative': "#0000ff",
'baseline': "#8e8e8e",
'subsective': "#606060",
'privative': "#393939... |
ab8eb7db9b2b83ed773208f736007d9e2d120383c1e715c409577db111ac05c6 | Python | 326 | 11 | # Module initialization
from .evaluation import compute_rmse, evaluate_model_with_nodes, log_to_csv, log_node_predictions_to_csv
from .train import train_lipophilicity_model
__all__ = [
'compute_rmse',
'evaluate_model_with_nodes',
'log_to_csv',
'log_node_predictions_to_csv',
'train_lipophilicity_mo... |
1352b7fd76bcee3de04a5b53c1d77006cdfc00ab82fd41fc170b5ee65b01e44c | Python | 327 | 15 | """Capture utilities: dump in-flight ``model.fit`` invocations to disk."""
from .recorder import (
DEFAULT_CAPTURE_DIR,
is_capture_enabled,
maybe_record_equator_fit,
maybe_record_fit,
)
__all__ = [
"DEFAULT_CAPTURE_DIR",
"is_capture_enabled",
"maybe_record_equator_fit",
"maybe_record_f... |
528fa866f66b62cb148f825c8192e19b28da043822ca1d597ff718b9091d219b | Python | 327 | 21 | # -*- coding: utf-8 -*-
"""TODO"""
import numpy as np
class Trace:
"""TODO"""
def __init__(self, size):
"""TODO"""
self.size = size
self.val = np.zeros(size)
def inc(self, i):
"""TODO"""
self.val[i] += 1
def set(self, i):
"""TODO"""
self.val[... |
1819ba225ed0e6fe2271a2d6f8349da4dbae231840cb37d629ddb9d7aff6fdf2 | Python | 328 | 10 | def drop_first_line(in_file):
import os
with open(in_file, 'r') as fin:
data = fin.read().splitlines(True)
fname = os.path.split(in_file)[-1]
with open(fname, 'w') as fout:
fout.writelines(data[1:]) # don't write the first line into the new file
return os.path.join(os.getcwd()... |
991e519ae29a937000e9b223baf1a387e08093defb50c18501415df70710f5eb | Python | 328 | 5 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['hmax_v3_adj'] = lambda: ModelCommitment(identifier='hmax_v3_adj', activations_model=get_model('hmax_v3_adj'), layers=get_layers('hmax_v3... |
bf30970373ac05dc2b6f5c305beba5f205bc07f18756704806991dfb0bcd6e97 | Python | 328 | 14 | import numpy
from setuptools import Extension, setup
setup(
ext_modules=[
Extension(
"seisbench.ext.utils",
sources=["seisbench/ext/utils.c"],
include_dirs=[numpy.get_include()],
extra_compile_args=["-O3", "-flto"],
extra_link_args=[],
),
... |
031218174d78b27f83b1b54c4885bac55d254226837fe31ea53a550b66f8c163 | Python | 330 | 10 | import matplotlib.pyplot as plt
from pathlib import Path
current_path = Path(__file__).parent
def apply_plotstyle():
style_path = current_path / 'paper.mplstyle'
style_path = style_path.resolve()
plt.style.use(str(style_path))
color_cycle = plt.rcParams['axes.prop_cycle'].by_key()['color']
return c... |
7a246216d9c1f3fc606fe580ccc44bad53d5e891ac33ab3190bdc815aded1df2 | Python | 330 | 6 | from brainscore_vision import benchmark_registry
from .benchmark import Maniquet2024ConfusionSimilarity, Maniquet2024TasksConsistency
benchmark_registry['Maniquet2024-confusion_similarity'] = lambda: Maniquet2024ConfusionSimilarity()
benchmark_registry['Maniquet2024-tasks_consistency'] = lambda: Maniquet2024TasksConsi... |
83b8b5ee499e068e5893e798d4ea890b0db73abfecb424fbf02ad416bd886a89 | Python | 330 | 12 | #!/usr/bin/env python
from storm_control.test.hal.standardHalTest import halTest
def test_hal_gui():
halTest(config_xml = "none_classic_config.xml",
class_name = "GUITest",
test_module = "storm_control.test.hal.gui_test",
show_gui = True)
if (__name__ == "__main__"):
test_... |
311a7ca20c2e2574e25deed70fc6530233ee343ea149adc5047ec3c471d49a9b | Python | 331 | 8 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, LAYERS
model_registry['alexnet_gated_submission'] = lambda: ModelCommitment(
identifier='alexnet_gated_submission',
activations_model=get_model(),
laye... |
4fc6d26c81e2d707712762bf510be5a26ea701c08f4c95fa148c49adfd894bee | Python | 331 | 10 | from tests.utils import pytest_importorskip
def test_qtpurchasing():
"""Test the qtpy.QtPurchasing namespace"""
QtPurchasing = pytest_importorskip("qtpy.QtPurchasing")
assert QtPurchasing.QInAppProduct is not None
assert QtPurchasing.QInAppStore is not None
assert QtPurchasing.QInAppTransaction i... |
587de55161c32fb7d90857e0a8c43feee0276c003685187e3bd4d6f05971b2d4 | Python | 331 | 9 | def run_matlab_script(script):
import subprocess
subprocess.run(f'matlab -nodisplay -nosplash -nodesktop -r "run(\'{script}\')"; exit;', shell=True)
return script
if __name__ == '__main__':
script = '/mnt/f/BIDS/demo_BIDS/derivatives/sepia_qsm/sub-YCHC0001/ses-01/sepia_qsm_script.m'
run_matlab_scr... |
8076344a58957520f841506b27828b4f2fa79ab0c6c82e759f3e050422d6c074 | Python | 331 | 13 | import torch
import celldetection as cd
import celldetection_scripts as cs
from skimage.data import coins
import pytest
def test_inference():
accelerator = 'cpu'
model = 'ginoro_CpnResNeXt101UNet-fbe875f1a3e5ce2c'
img = coins()
print('Image:', img.shape)
cs.cpn_inference(img, model, accelerator=ac... |
83cb7cb308827a6449938f4e8dff4522f40cc792485eeb747c1fbce5ab216309 | Python | 331 | 8 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, LAYERS
model_registry['alexnet_pipeline_test_v1'] = lambda: ModelCommitment(
identifier='alexnet_pipeline_test_v1',
activations_model=get_model(),
laye... |
bb99a035c8b46b21b040c9d2ca35d9a98722fca44b629a739437c4e5f8a765e1 | Python | 331 | 21 | # flake8: noqa
from .link_pred import (
LinkPredPrecision,
LinkPredRecall,
LinkPredF1,
LinkPredMAP,
LinkPredNDCG,
LinkPredMRR,
)
link_pred_metrics = [
'LinkPredPrecision',
'LinkPredRecall',
'LinkPredF1',
'LinkPredMAP',
'LinkPredNDCG',
'LinkPredMRR',
]
__all__ = link_pr... |
df34bf7d2548a9fc2a98fa64af182bb3547383a9218d9ddcfe1eae014eba2992 | Python | 331 | 8 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, LAYERS
model_registry['alexnet_workflow_trigger'] = lambda: ModelCommitment(
identifier='alexnet_workflow_trigger',
activations_model=get_model(),
laye... |
e5c4c7099d251ef863e1dcbb074bbefdffb8897ca8d12e464149ee6d57458dd4 | Python | 332 | 11 | import brainscore_vision
def test_has_identifier():
model = brainscore_vision.load_model('gabor_filter_entropy_4x4')
assert model.identifier == 'gabor_filter_entropy_4x4'
def test_has_default_layer():
model = brainscore_vision.load_model('gabor_filter_entropy_4x4')
assert model.layers == ['gabor_ent... |
05c2507583fdd421480f7f41b3fc01e592c34505af0bc6fec1f45b283acaed92 | Python | 333 | 13 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from django.http import HttpRequest
from rest_framework.request import Request
from cvat.apps.engine.middleware import WithUUID
from cvat.apps.iam.middleware import WithIAMContext
class ExtendedRequest(HttpRequest, Request, WithUUID, WithIAMContex... |
354c610caa19e47191566b39cb1efb3d0cea6b752c233a207844cfc576c27aeb | Python | 333 | 7 | from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from brainscore_vision import model_registry
from .model import get_layers,get_model
model_registry['BiT-S-R50x1'] = \
lambda: ModelCommitment(identifier='BiT-S-R50x1', activations_model=get_model('BiT-S-R50x1'), layers=get_layers('B... |
bfc270059f7a43ba7e97426fe4253a1d62d12f1462ce1a8754745e78a50e9f1b | Python | 333 | 7 | from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from brainscore_vision import model_registry
from .model import get_layers,get_model
model_registry['BiT-S-R50x3'] = \
lambda: ModelCommitment(identifier='BiT-S-R50x3', activations_model=get_model('BiT-S-R50x3'), layers=get_layers('B... |
c7bcb0df08a43523ad4b46cf9a861a3690dbf74ed07c91553a9d4dbe1942f437 | Python | 333 | 8 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, LAYERS
model_registry['alexnet_gated_submission2'] = lambda: ModelCommitment(
identifier='alexnet_gated_submission2',
activations_model=get_model(),
la... |
3ee87341f4f32e3714d01d25ed3a808ef174fc6924a73f7adceae9bc5bace1bc | Python | 334 | 12 | import pytest
from qtpy import PYSIDE2
@pytest.mark.skipif(PYSIDE2, reason="Not available in CI")
def test_qtserialport():
"""Test the qtpy.QtSerialPort namespace"""
QtSerialPort = pytest.importorskip("qtpy.QtSerialPort")
assert QtSerialPort.QSerialPort is not None
assert QtSerialPort.QSerialPortInf... |
7125cb2e6f878ad0bda31d8064d4402515d9a3b0a94c8ccf14486a7719cbcc6d | Python | 334 | 8 | from brainscore_vision import metric_registry
from .ceiling import InternalConsistency
from brainscore_vision.metric_helpers.temporal import PerTime
metric_registry['internal_consistency'] = InternalConsistency
metric_registry['internal_consistency_temporal'] = lambda *args, **kwargs: PerTime(InternalConsistency(*ar... |
728a5aa9cb40c622c5b6e91e60697790d97c20c7c4ab8cc74dadcf7ecf4bf0d1 | Python | 334 | 11 | #!/usr/bin/env python
from storm_control.test.hal.standardHalTest import halTest
def test_hal_lock_config1():
halTest(config_xml = "none_config_lock_check.xml",
class_name = "LockConfigTest1",
test_module = "storm_control.test.hal.config_tests")
if (__name__ == "__main__"):
test_hal_l... |
40523aa8334d1d03fac599607ceab9fc3a62f59eb9aa4d800b3035c0f71b9ec7 | Python | 335 | 8 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, LAYERS
model_registry['alexnet_private_submission'] = lambda: ModelCommitment(
identifier='alexnet_private_submission',
activations_model=get_model(),
... |
8b2f7acbdc487e4b5fe41d0eb20489260dcf6a7553af6db5471dfb4baaca15c3 | Python | 335 | 14 | import glob
# NOTE !!!
#edit dirs as needed
outFile = open('trfDir/trfchunks.cmds','w')
fl = glob.glob('/home/jmkidd/links/kidd-lab/jmkidd-projects/tasha/mask-repeats/chunk-segments/*fa')
print('have %i fl' % len(fl))
for f in fl:
cmd = 'trf %s 2 7 7 80 10 50 500 -h -d \n' % f
outFile.write(cmd)
# break
ou... |
d621fa8df5526f27c7c833a2871dd16cb1987cd7549488979b04619e899a8beb | Python | 335 | 11 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import datetime
MAX_EVENT_DURATION = datetime.timedelta(seconds=100)
WORKING_TIME_RESOLUTION = datetime.timedelta(milliseconds=1)
WORKING_TIME_SCOPE = "send:working_time"
USER_ACTIVITY_SCOPE = "user:activity"
COMPRESSED_EVENT_SCOPES = frozenset(("ch... |
f409bfb783ad71c91f90bef4aeca9dc672bcf3ed671a2e67407678a9c29c79ca | Python | 335 | 17 | # This file is part of connectome-manipulator.
#
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024 Blue Brain Project/EPFL
"""Testing helpers"""
import pytest
@pytest.fixture
def nodes():
class FakeNode:
config = {"morphologies_dir": "/foo/bar"}
_population = None
return (FakeNode(... |
194011a6ce597e8a6579c58184ea68a9c000abdf5f44e1547c426286c3cc32c2 | Python | 337 | 7 | from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from brainscore_vision import model_registry
from .model import get_layers,get_model
model_registry['densenet-169'] = \
lambda: ModelCommitment(identifier='densenet-169', activations_model=get_model('densenet-169'), layers=get_layers... |
331edf89923eb1ae8eb61e8441218b044711dd5739b36709ee118b55ed843bd2 | Python | 337 | 7 | from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from brainscore_vision import model_registry
from .model import get_layers,get_model
model_registry['densenet-121'] = \
lambda: ModelCommitment(identifier='densenet-121', activations_model=get_model('densenet-121'), layers=get_layers... |
43b2f85755187b49f1090aa0c190143eddfa7312f2a6fb5947842db8f7b9cf32 | Python | 337 | 7 | from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from brainscore_vision import model_registry
from .model import get_layers,get_model
model_registry['BiT-S-R152x4'] = \
lambda: ModelCommitment(identifier='BiT-S-R152x4', activations_model=get_model('BiT-S-R152x4'), layers=get_layers... |
4753c96843e026cb642bbdb5c98ba561124482554a4538c95bc8c73dcaa6a148 | Python | 337 | 7 | from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from brainscore_vision import model_registry
from .model import get_layers,get_model
model_registry['inception_v1'] = \
lambda: ModelCommitment(identifier='inception_v1', activations_model=get_model('inception_v1'), layers=get_layers... |
c3d94c1dda2eb46754ef3101614c0ea554ce2e00608b77d04955829a4ae07345 | Python | 337 | 10 | import numpy as np
from ivim.constants import ybar, y, Db
# Test functions
def test_y():
np.testing.assert_approx_equal(ybar, 42.58e6, 4) # Hz/T
np.testing.assert_approx_equal(y, 268e6, 3) # rad/T/s
def test_D():
np.testing.assert_allclose(Db, 1.75e-3, rtol = 0.1, atol = 0.1e-3) # Diffusion coefficie... |
d936433a0ac7d46d3f82527b82bff2761c479a523cbb0d9a65c70ad1e8d43a98 | Python | 337 | 7 | from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from brainscore_vision import model_registry
from .model import get_layers,get_model
model_registry['BiT-S-R101x3'] = \
lambda: ModelCommitment(identifier='BiT-S-R101x3', activations_model=get_model('BiT-S-R101x3'), layers=get_layers... |
db1994ed995ef6951af7177ce615540e08ed1af39fa0931f0c1c7b870531698d | Python | 337 | 7 | from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from brainscore_vision import model_registry
from .model import get_layers,get_model
model_registry['BiT-S-R152x2'] = \
lambda: ModelCommitment(identifier='BiT-S-R152x2', activations_model=get_model('BiT-S-R152x2'), layers=get_layers... |
e9ac08d1e86cb8a11151760b5f3ee6a999f880506f91fc2dccdafb9179cb195e | Python | 337 | 7 | from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from brainscore_vision import model_registry
from .model import get_layers,get_model
model_registry['BiT-S-R101x1'] = \
lambda: ModelCommitment(identifier='BiT-S-R101x1', activations_model=get_model('BiT-S-R101x1'), layers=get_layers... |
f409e63d96161473825014a280980dbda3261f2bd5f4b8ecebd06f7cc296cb46 | Python | 337 | 8 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, LAYERS
model_registry['cv_18_dagger_408_pretrained'] = lambda: ModelCommitment(
identifier='cv_18_dagger_408_pretrained',
activations_model=get_model(),
... |
24455d98ff077abdb5901ed409552a3210d970204118d08d22b74d27d9da6d20 | Python | 338 | 13 | import sys
import subprocess
from .resources import get_path
EXECUTABLE_EXTENSION = '.exe' if sys.platform == 'win32' else ''
BMBI_EXECUTABLE = get_path('native' + EXECUTABLE_EXTENSION)
def main():
result = subprocess.run([BMBI_EXECUTABLE, 'hydrate'] + sys.argv[1:])
sys.exit(result.returncode)
if __name__ == '__... |
54c5f2469b01243b4b73b6748e62b9fab20f1efe84390d16931533450db65675 | Python | 338 | 14 | """
Utility functions for the orientation-dependent qMRI project
Author: florian.birk@tuebingen.mpg.de
"""
# Import main functions for easier access
from . import paths
from .io import *
from .processing import *
from .analysis import *
from .models import *
from .visualization import *
from .simulation import *
from ... |
7760c236e08c765847dc191be0ad7994871c08c39df67459d42f0b0cf42ea5bc | Python | 338 | 14 | """
BIDS data handling.
- Currently only implemented as a draft in the gui functions
- Will be outsourced to this file in the future
"""
#import nibabel as nib
#from bids import BIDSLayout
#from nilearn import datasets, maskers
#from nilearn.interfaces.fmriprep import load_confounds
def load_fmriprep_outputs(bids_... |
d14688f493da5ea88ad29c433c4bd19b99f2cc56e5b09a134c770289896ff591 | Python | 338 | 9 | from Bio.PDB import PDBParser
from Bio.PDB.DSSP import DSSP
p = PDBParser()
structure = p.get_structure("", 'dataset/Ago/sample_1.pdb')
model = structure[0]
dssp = DSSP(model, "dataset/Ago/sample_1.pdb",dssp='mkdssp')
sec_structures = [dssp_res[2] for dssp_res in dssp]
sec_structure_str = ''.join(sec_structures)
print(... |
0ccaa9cea930c86090505308a9309be133374bc47a5c043a8793c7ecdb1f13c4 | Python | 339 | 19 | # general imports
from .components import *
from . import styles
from . import colours
# component imports
from . import ribbon
from . import dist
from . import means
from . import significance
from . import cosmetics
from . import graphs
# modelling imports
from . import model
from . import link
# maths imports
fro... |
844f35d53fb46d9d9616f4f4f9591270338c42c5b7a8f3eed2396c6e95e14a25 | Python | 339 | 12 | from argclz.commands import parse_command_args
from .main_concat_csv import ConcatCellCSVOptions
from .main_dff_session import DffSesOption
parse_command_args(
usage='python -m rscvp.signal CMD ...',
description='raw calcium signal information',
parsers=dict(
ds=DffSesOption,
csv=ConcatCell... |
c060e0da221ccecf1c572586bead1a084bba4004bf97f7dc1504b85fb527f8ea | Python | 339 | 14 | # yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook', by_epoch=True),
dict(type='TensorboardImageLoggerHook', by_epoch=True),
])
# yapf:enable
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
cudn... |
54e2399f5f695a9b741bb9d35570c5515a0982d710bd339dcc071822fc67af14 | Python | 340 | 13 | from .gnn import GraphUNet
def initialize_GNN_model(size_hidden_layers=64):
model = GraphUNet(
in_channels=8,
mlp_hidden_channels=256,
out_channels=1,
size_hidden_layers = size_hidden_layers,
dim_enc = size_hidden_layers,
mlp_depth = 4,
bn_bool = True,
... |
188cada56b0c50fd5243ca32b0b45ad586419f2379b435917b52dea8fa2778ed | Python | 341 | 10 | import pytest
import brainscore_vision
@pytest.mark.travis_slow
@pytest.mark.parametrize('field_of_view', ['fov4', 'fov12', 'fov16'])
def test_has_identifier(field_of_view):
model = brainscore_vision.load_model(f'alexnet_training_seed_01_{field_of_view}')
assert model.identifier == f'alexnet_training_seed_01_... |
60ab809d4b037e7e577920a96a1dc2cb747387cbaf7a2a5dc9dade2f682d363a | Python | 341 | 21 | # -*- coding: utf-8 -*-
"""TODO"""
import numpy as np
class Trace2D:
"""TODO"""
def __init__(self, size):
"""TODO"""
self.size = size
self.val = np.zeros(size)
def inc(self, i, j):
"""TODO"""
self.val[i, j] += 1
def set(self, i, j):
"""TODO"""
... |
bced255d2ca3cb1c00e82884b397363a144140912fff21b2311595353677f7b5 | Python | 341 | 13 | import dtiplayground.dmri.common as common
import dtiplayground.dmri.common.dwi as dwi
import dtiplayground.dmri.common.pipeline as pipeline
logger=common.logger
measure_time=common.measure_time
Color=common.Color
get_uuid=common.get_uuid
object_by_id=common.object_by_id
get_timestamp=common.get_timestamp
dwi = dwi
... |
c4aa1182af1b94d0c52998f7acccc7c7b186f4cb49d9c321090b121773cd29eb | Python | 341 | 6 | # flake8: noqa
from deepreg.dataset.loader.grouped_loader import GroupedDataLoader
from deepreg.dataset.loader.h5_loader import H5FileLoader
from deepreg.dataset.loader.nifti_loader import NiftiFileLoader
from deepreg.dataset.loader.paired_loader import PairedDataLoader
from deepreg.dataset.loader.unpaired_loader impor... |
d67787f932b5888b2aaf44097e0be4c2d9725afccdb587e7d2f738ba6bdb07f7 | Python | 341 | 16 | from .key_dataset import KeyDataset
from .conformer_sample_dataset import (
ConformationSampleDataset,
ConformationExpandDataset,
)
from .lmdb_dataset import LMDBDataset, StackedLMDBDataset
from .pcq_dataset import (
PCQDataset,
)
from .oc20_dataset import (
Is2reDataset,
)
from .data_utils import nump... |
4ffd1679938564726ef23e2552cab5ad491a208584b9a71fcff92051d4532395 | Python | 343 | 9 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry["mvimgnet_rf"] = lambda: ModelCommitment(
identifier="mvimgnet_rf",
activations_model=get_model("mvimgnet_rf"),
layers=get_la... |
7fadd52bd62e4108f414f6b2d9c1cd2c9219e8526c18e247f821819874f50c5f | Python | 343 | 15 | # config.py
import os
from connattractor import utils
import glob
ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) # This is your Project Root
MIST122_NII = utils.get_MIST_nii_path()
MIST122_LABELS_TSV = utils.get_MIST_labels_path()
DEFAULT_ATLAS_NII = MIST122_NII
DEFAULT_ATLAS_LABELS_TSV = MIST122_LABELS_TSV... |
8b3b618f1488500a232e39f810f4072c6a2695bdbb2d14de12c5f4224d07436e | Python | 343 | 8 | from brainscore_vision import benchmark_registry
from .benchmark import Bracci2019RSA
# benchmark_registry['Bracci2019_RSA-V1'] = lambda: _Bracci2019RSA("V1")
# benchmark_registry['Bracci2019_RSA-posteriorVTC'] = lambda: _Bracci2019RSA("posteriorVTC")
benchmark_registry['Bracci2019.anteriorVTC-rdm'] = lambda: Bracci20... |
92a78f6dd450b74a7ade0b9df6ebedc9d889a80dff6d2f08b251cfd8742b93c2 | Python | 343 | 13 | """Collection of sampling Distributions.
Intended to serve as priors for `nems.layers.base.Parameter`.
Modified from code by Dr. Brad Buran (github: bburan).
"""
from .normal import Normal
from .half_normal import HalfNormal
from .uniform import Uniform
from .beta import Beta
from .gamma import Gamma
from .exponent... |
a8e1d3c0dad87c26ed32991d5a3ed534f49c054cb3cd6ae10026254631556697 | Python | 343 | 13 | """
This is a legacy schema that will no longer be used in the unified pipeline.
It is provided here to support the migration process.
"""
import datajoint as dj
from distutils.version import StrictVersion
assert StrictVersion(dj.__version__) >= StrictVersion('0.2.5')
schema = dj.schema('common', locals())
schema.s... |
74f2549d6ea34c8fd2fef83bfedc08acb27a02be00f66092f424872bf37fcf7c | Python | 344 | 14 | """
Tools and data structures for working with genomic intervals (or sets of
regions on a line in general) efficiently.
"""
# For compatiblity with existing stuff
from bx.intervals.intersection import (
Intersecter,
Interval,
IntervalNode,
IntervalTree,
)
__all__ = ["Intersecter", "Interval", "Interva... |
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