sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
c3146fb3c79a86be3a2784df554cdbd4efd98acd641504a89a316dbfcf5fa84f | Python | 344 | 14 | from __future__ import annotations
from PySide6 import QtGui
from PySide6 import QtWidgets
class StatusStats(QtWidgets.QLabel):
def __init__(self) -> None:
super().__init__("")
font = QtGui.QFont()
font.setFamily("monospace")
font.setStyleHint(QtGui.QFont.StyleHint.Monospace)
... |
f1d8fc7ad12da19a4cdb5a09e0932dbef7a7d69b1979cf194d677ac60dfb0d4c | Python | 344 | 11 | """
Generalized data dimensions for multi-purpose MRI image reconstruction.
"""
DIM_X = -1 # readout
DIM_Y = -2 # phase-encoding
DIM_Z = -3 # slice
DIM_COIL = -4 # coil
DIM_ECHO = -5 # For diffusion MRI, it stores the shots per DWI
DIM_TIME = -6 # For diffusion MRI, it stores the diffusion encodings
DIM... |
13153128e4b5b4d3ec4948b02dd1f59d09b4d28fb36d84301a025aaff8cc26fc | Python | 345 | 9 | from .basic_distributions import Distribution
from .bernoulli import Bernoulli
from .univariate_gaussian import UnivariateGaussian
from .bernoulli_gaussian import BernoulliGaussian
from .beta import Beta
from .binomial import Binomial
from .gamma import Gamma
from .multivariate_gaussian import MultivariateGaussian
from... |
ea57f338bbe388b6557ab6d2efe898e808f71b99cf7ce016717601363735b973 | Python | 345 | 10 | import brainscore_vision
import pytest
@pytest.mark.parametrize('assembly_identifier', [
pytest.param('Rajalingham2018.public', marks=[]),
pytest.param('Rajalingham2018.private', marks=[pytest.mark.private_access]),
])
def test_existence(assembly_identifier):
assert brainscore_vision.load_dataset(assembly... |
0f7205954a78e47d661214bd4e345fe6d672c0b293a611d6049b8f7bd93d3979 | Python | 346 | 17 | # Generated by Django 5.2.14 on 2026-07-30 15:37
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
("engine", "0106_add_interval_annotations"),
]
operations = [
migrations.RemoveField(
model_name="annotationguide",
name="is_... |
62dba273d5cf1c6866619fae0c0c2a6c8e43fa10e1ade4164886f79405013d8b | Python | 347 | 15 | #!/usr/bin/env python
from storm_control.test.hal.standardHalTest import halTest
def test_hal_tcp_slt1():
halTest(config_xml = "none_tcp_config.xml",
class_name = "SetLockTarget1",
show_gui = True,
test_module = "storm_control.test.hal.tcp_tests")
if (__name__ == "__main__"... |
d95ab869912b8c8f18e03c1732c69f12171e78c03c017098d009eebd29d45fcd | Python | 347 | 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['convnextv2-tiny'] = lambda: ModelCommitment(identifier='convnextv2-tiny', activations_model=get_model('convnextv2-tiny'), layers=get... |
e7bb653f02274beac4771652f33b86822ee3ed6856ee92210e4224d7ee8ed432 | Python | 347 | 15 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from django.urls import include, path
from rest_framework import routers
from . import views
router = routers.DefaultRouter(trailing_slash=False)
router.register("requests", views.RequestViewSet, basename="request")
urlpatterns = [
path("api/"... |
15f3d7a69c0cd9a529bfa0e38d555ce388615fca6ccc369b7281c6848f405794 | Python | 348 | 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['Res2Net50_26w_4s'] = lambda: ModelCommitment(identifier='Res2Net50_26w_4s', activations_model=get_model('Res2Net50_26w_4s'), layers=get_... |
29862a516e46e21c173cf6757b8a6bbbaece2039329508fd5aa23c533d43ae4c | Python | 348 | 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['compact_vgg19_V4'] = lambda: ModelCommitment(identifier='compact_vgg19_V4', activations_model=get_model('compact_vgg19_V4'), layers=get_... |
2f7d0697c9a6ef4c7f61f93ca2dfb7c6c7b227fc01cded63345fb03df842ef7c | Python | 348 | 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['compact_vgg19_IT'] = lambda: ModelCommitment(identifier='compact_vgg19_IT', activations_model=get_model('compact_vgg19_IT'), layers=get_... |
38c634d7d9fe129a641bbc683e016e20df89b0e038d4d31659e118191172234e | Python | 348 | 8 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
proteinseq_toks = {
'toks': ['L', 'A', 'G', 'V', 'S', 'E', 'R', 'T', 'I', 'D', 'P', 'K', 'Q', 'N', 'F', 'Y', 'M', 'H', 'W', 'C', 'X', 'B',... |
4a7add27853c01a8b312d2c80c4ed9b0fd511daa813a375a28867303d1da3cfc | Python | 348 | 13 | from pcafeat.select_pca import select_pca_features
from pcafeat.select_ttest import select_ttest_features
from pcafeat.pca_feature_select import pca_extract, con_extract, select_optimal_pcs
__version__ = "1.2.0"
__all__ = [
"select_pca_features",
"select_ttest_features",
"pca_extract",
"con_extract",
... |
4bcb3ada3556472eb2302eb45cf502780d72939db168352265ca2b8893d0de6c | Python | 348 | 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['effnetb1_272x240'] = lambda: ModelCommitment(identifier='effnetb1_272x240', activations_model=get_model('effnetb1_272x240'), layers=get_... |
7e9bd094a37dd87ee56fb1c0c037f747e23d4326d563a588e2b0f3c64d7907a2 | Python | 348 | 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['Soumyadeep_inf_1'] = lambda: ModelCommitment(identifier='Soumyadeep_inf_1', activations_model=get_model('Soumyadeep_inf_1'), layers=get_... |
e8fbcc1b2ac52c83dde1fd31449d0cea9e0e62c6acc5094243d491d6150a6016 | Python | 348 | 20 | import sys
import time
sys.path.append("../../")
import jieba
jieba.enable_parallel()
url = sys.argv[1]
content = open(url,"rb").read()
t1 = time.time()
words = "/ ".join(jieba.cut(content))
t2 = time.time()
tm_cost = t2-t1
log_f = open("1.log","wb")
log_f.write(words.encode('utf-8'))
print('speed %s bytes/second'... |
44b91525975a5b24aa496efde4e7036bced22f816b8e5671f046db004974a0e7 | Python | 349 | 9 | """
This package contains an implementation of UNRAVEL, an algorithm used to combine
macroscopic tractography information with microscopic multi-fixel model
estimates in order to improve the accuracy in the estimation of the
microstructural properties of neural fibers in a specified tract.
"""
__version__ = "1.7.5"
__... |
54bcc60f6d2e72fa93b03d827e03f8266b4aca1c6ef3e33d8086406f19c9bb2e | Python | 349 | 12 | from pathlib import Path
from deep_events.database import get_collection
import pymongo
import yaml
FILEPATH = Path(__file__).parents[0]
with open(FILEPATH / "settings.yaml", "r") as stream:
COLLECTION = yaml.safe_load(stream)["COLLECTION"]
def test_connection():
assert isinstance(get_collection(COLLECTION),... |
57d7dfac8292aa10f0eb1519d76964b927cdb38f1c429d84789e99d7d19f9d5c | Python | 349 | 14 | import torch
from torch_geometric import is_compiling
def is_in_onnx_export() -> bool:
r"""Returns :obj:`True` in case :pytorch:`PyTorch` is exporting to ONNX via
:meth:`torch.onnx.export`.
"""
if is_compiling():
return False
if torch.jit.is_scripting():
return False
return to... |
765220f735564f8c0421f607a67d49860ad897380a6092dce8bec4ac71cbf58e | Python | 349 | 7 | # Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .logger import get_root_logger
from .position_encoding import SinePositionalEncoding, LearnedPositionalEncoding
from .color_depth import colorize
__all__ = ['get_root_logger', 'collect_env', 'SinePositionalEncoding', 'LearnedPosi... |
7d4f05c7a88f814c43323fe6bc84086cad98d1975dc3f00703032f0d4f97560a | Python | 349 | 9 | """
This package contains an implementation of UNRAVEL, an algorithm used to combine
macroscopic tractography information with microscopic multi-fixel model
estimates in order to improve the accuracy in the estimation of the
microstructural properties of neural fibers in a specified tract.
"""
__version__ = "1.7.4"
__... |
e3d8b4232ec2db289a5617f80ce7c3804edb3c0a3c184b167b9f7ac41971f6c4 | Python | 349 | 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['efficientnet-b7'] = \
lambda: ModelCommitment(identifier='efficientnet-b7', activations_model=get_model('efficientnet-b7'), layers=g... |
02cd4fd753db5df1c1318190b40ab7ecc02318cb5f7e5f42021794a17e4bde2e | Python | 350 | 19 | """# Log slider widget
A logarithmic scale range slider widget.
"""
from magicgui import magicgui
@magicgui(
auto_call=True,
result_widget=True,
input={"widget_type": "LogSlider", "max": 10000, "min": 1, "tracking": False},
)
def slider(input=1):
"""Logarithmic scale slider."""
return round(inpu... |
0befff4b7a59c5e83dc2971b7332614453c60de050c255c552cdfe40b2a271f4 | Python | 350 | 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['antialiased-r50'] = \
lambda: ModelCommitment(identifier='antialiased-r50', activations_model=get_model('antialiased-r50'), layers=g... |
b0b61d1dc0424c0e4000c81b677651ba32d46e1185c1ee88ce946138bb64ecfa | Python | 350 | 12 | """The only production construction site for ``ChatAnthropic``."""
from __future__ import annotations
from typing import Any
from langchain_anthropic import ChatAnthropic
def build(client_kwargs: dict[str, Any]) -> ChatAnthropic:
"""Construct a ``ChatAnthropic`` from prepared keyword arguments."""
return C... |
09484a1d3fa259605e7696ceaacf6eee42c62052408479db35a758ccc89b18be | Python | 351 | 10 | import brainscore_vision
import pytest
@pytest.mark.parametrize('assembly_identifier', [
pytest.param('Kar2018hvm', marks=[pytest.mark.private_access]),
pytest.param('Kar2018cocogray', marks=[pytest.mark.private_access]),
])
def test_existence(assembly_identifier):
assert brainscore_vision.load_dataset(as... |
366913edb2adde5da7436333d20e6aab8130a4653f5b5613ddb8cf7d3401d8e2 | Python | 351 | 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["eMMCR_Vanilla"] = lambda: ModelCommitment(
identifier="eMMCR_Vanilla",
activations_model=get_model("eMMCR_Vanilla"),
layers=... |
8c0ff80675ffe0aac1e386ec15a4cfff3512a7ec0be713cce73b536af7ce9da4 | Python | 351 | 14 | import numpy as np
import numpy.testing as npt
import AFQ.utils.stats as AFS
def test_contrast_index():
x1 = np.asarray([1, 2, 3.0, 1.1])
x2 = np.asarray([0.9, 3.0, 5.0, 0.8])
contrast_index1 = AFS.contrast_index(x1, x2)
npt.assert_almost_equal(
contrast_index1, [0.05263158 * 2, -0.2 * 2, -0... |
cbd11097647979b22c1e9234c91dded095ae5e5bac41b5ee8d05ec63ac19ab3e | Python | 351 | 11 | from typing import TypedDict, Annotated
from langgraph.graph import add_messages
class ManagerWorkerState(TypedDict):
messages: Annotated[list, add_messages]
worker_result: Annotated[list, add_messages]
current_task_index: int
task_list: list
worker_channel: dict[str, Annotated[list[str], add_mess... |
d42baeace2fb205151dffe670b2b07d4978ffcd0bfa168e7293385f62a64391b | Python | 351 | 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-v1.0.1'] = lambda: ModelCommitment(identifier='model-lecs-v1.0.1', activations_model=get_model('model-lecs-v1.0.1'), layers=g... |
e64dfcb82766dfb78649bb09133beaaa32cc7ad1bb957801bd072d44b1555a20 | Python | 351 | 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["eMMCR_lmda_01"] = lambda: ModelCommitment(
identifier="eMMCR_lmda_01",
activations_model=get_model("eMMCR_lmda_01"),
layers=... |
eed614c3a5748adb8c1f40ea0c8283c4b9cfe341229a75313bb612ffd84ea382 | Python | 351 | 19 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
("consensus", "0001_initial"),
]
operations = [
migrations.RemoveField(
model_name="consensussettings",
n... |
04006fcd327dd83d2f5c18a9839a1ed7c5dd88d8412936ebc626bb5d743220fa | Python | 352 | 15 | import requests
def send_request(url: str, payload: list[dict]) -> dict:
response = requests.post(url, json=payload)
response.raise_for_status()
return response.json()
payload = [
{"imdb_reviews": "This movie was great! I loved it!"},
]
response = send_request(url="http://localhost:8000/predict", p... |
05e99443349e165db474a2996be2b752bf8374b9099dfb27c6c25c9ff221f661 | Python | 352 | 12 | # -*- coding: utf-8 -*-
from . import dropout
from .bert import BertEmbedding
from .biaffine import Biaffine
from .trilinear_attention import TrilinearScorer
from .bilstm import BiLSTM
from .char_lstm import CHAR_LSTM
from .mlp import MLP
__all__ = ['CHAR_LSTM', 'MLP', 'BertEmbedding',
'Biaffine', 'BiLSTM'... |
1574215da7a6174ae2f26592454acdbef1651691ce5eaf6bcbacad4296d73f17 | Python | 352 | 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['chresmax_v3_cl_01'] = lambda: ModelCommitment(identifier='chresmax_v3_cl_01', activations_model=get_model('chresmax_v3_cl_01'), layers=g... |
5e6cd653c518b61ea3a9a69dd948d8691597b1851bf31aec5828d7d586e76a20 | Python | 352 | 16 | """# Optional user choice
Optional user input using a dropdown selection widget.
"""
from magicgui import magicgui
# Using optional will add a '----' to the combobox, which returns "None"
@magicgui(path={"choices": ["a", "b"]})
def f(path: str | None = None):
"""Öptional user input function."""
print(path, ... |
9912c3aee0ba6de7de4b8ac223521f2d68930fac944ec3e9989fccdfb7011bf7 | Python | 352 | 13 | """Classes defining configurations for pyment training and inference."""
from .finetuning_configuration import FinetuningConfiguration
from .target_configuration import (
CategoricalTargetConfiguration,
compile_target_encoder,
)
__all__ = [
'FinetuningConfiguration',
'CategoricalTargetConfiguration',
... |
addc408b6cb0ef0d2340d4551038437fba564216061e9666a9580687479b73ed | Python | 352 | 16 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from django.apps import AppConfig
class EventsConfig(AppConfig):
name = "cvat.apps.events"
def ready(self):
from cvat.apps.iam.permissions import load_app_iam_rules
load_app_iam_rules(self)
from . import signals ... |
bf0385f0a5f272bfe29b7bdc7f31619f001eba74ec3e5a591271bc67d197c51e | Python | 352 | 9 |
class ColumnNameChangeWarning(UserWarning):
"""Warning that causallib renamed input name
to ensure all columns are of a single type
so scikit-learn>=1.2.0 is happy.
See array validation:
https://github.com/scikit-learn/scikit-learn/blob/8133ecaacca77f06a8c4c560f5dbbfd654f1990f/sklearn/utils/valida... |
d295a49977d9b5b76bf802b09628b92d3160813107f8c0ab48aa59c6bb4f955b | Python | 352 | 12 | import warnings
import torch_geometric.contrib.transforms # noqa
import torch_geometric.contrib.datasets # noqa
import torch_geometric.contrib.nn # noqa
import torch_geometric.contrib.explain # noqa
warnings.warn(
"'torch_geometric.contrib' contains experimental code and is subject to "
"change. Please us... |
d7c6087166ce2ff72408aadd201a1ffbefa5beff531d805952332bc3034fc1ff | Python | 352 | 9 | from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from brainscore_vision import model_registry
from .model import get_layers,get_model
model_registry['imagenet_l2_3_0'] = \
lambda: ModelCommitment(identifier='imagenet_l2_3_0', activations_model=get_model('imagenet_l2_3_0'), layers=g... |
dfee5aa87d4fb795e76226e7e2c5efc9b37121b0ba702412f1f27c76304971f8 | Python | 352 | 15 | """Convert a surface to a triangular mesh."""
import SVMTK as svm
from pathlib import Path
if __name__ == "__main__":
print("Start ",__file__)
outdir = Path("results")
outdir.mkdir(exist_ok=True)
p = svm.Surface("../Data/P.off")
p.triangulate_faces()
p.save(str(outdir/"triangularP.stl"))
... |
ea10092ef6787ed433334d0759854b104696e1698dfc90c12f54d1f010d429e7 | Python | 352 | 12 | from __future__ import annotations
import numpy as np
def sigma(x: np.ndarray) -> np.ndarray:
return 1.0 / (1.0 + np.exp(-x))
def p_veridical(P: np.ndarray, G: np.ndarray, alpha: float = 1.0, beta: float = 1.0) -> np.ndarray:
"""P(M=veridical|S) = sigma(alpha*P - beta*G)."""
return sigma(alpha * np.asa... |
441e057e23571e258d6f930715456521d9368080c6ad9a2547b57f131b85a801 | Python | 353 | 15 | import requests
def send_request(url: str, payload: list[dict]) -> list[dict]:
response = requests.post(url, json=payload)
response.raise_for_status()
return response.json()
payload = [
{"english": "", "spanish": "Tengo mucho hambre"},
]
response = send_request(url="http://localhost:8000/predict", ... |
bbb8b988bea8fe36574fee195aa4ad66a09125d5de0e07317559f98a6d9e42ef | Python | 353 | 14 | # -*- coding: utf-8 -*-
import sys
sys.path.append('..')
from nerpy.ner_model import NERModel
def test_diff():
a = '研究团队面向国家重大战略需求追踪国际前沿发展借鉴国际人工智能研究领域的科研模式有效整合创新资源解决复'
b = '英汉互译比较语言学'
print(a)
assert a is not None
|
f6e8179723b710decb0d50efaca18fc83cb6079f81a0d8067bce0b365b031e0f | Python | 354 | 15 | import inspect
from result_caching import is_iterable
def fullname(obj):
module = obj.__module__
name = obj.__name__ if inspect.isfunction(obj) else obj.__class__.__name__
return module + "." + name
def make_list(element):
if not is_iterable(element) or isinstance(element, (str, bytes)):
re... |
41518a88a3ec418ad264d3c3d68588c213e39e5e1841c07b074edfda7c278959 | Python | 355 | 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_ms_05"] = lambda: ModelCommitment(
identifier="mvimgnet_ms_05",
activations_model=get_model("mvimgnet_ms_05"),
laye... |
520cce2bc2f89f0994afa31db7f42abbfb623ac9d36e31bf8324d0e0a7120a69 | Python | 355 | 15 | # Copyright (C) 2020-2021 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from django.apps import AppConfig
class LambdaManagerConfig(AppConfig):
name = "cvat.apps.lambda_manager"
def ready(self) -> None:
from cvat.apps.iam.permissions import load_app_iam_rules... |
8a9ffd9083e722c5c2061eca9a3b1fd1a29ad58bc14d6be3c917b9e6ab1cc849 | Python | 355 | 10 | """Loader factories used by the LAION-fMRI data registry.
Kept in a subfolder so the main ``data/laion_fmri/__init__.py`` stays a
simple, grep-friendly registry.
"""
from .assemblies import make_persubject_loader, make_shared_loader
from .stimuli import load_stimulus_set
__all__ = ["load_stimulus_set", "make_persubj... |
c5bb467164ec77345591c58f095537800a4df034ebb8d5fdd68307474352f89b | Python | 355 | 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_ss_00"] = lambda: ModelCommitment(
identifier="mvimgnet_ss_00",
activations_model=get_model("mvimgnet_ss_00"),
laye... |
f77a9492b269f35018440b1327521ce664fb514f4285c6cd03463f8c1165ac0a | Python | 355 | 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_ss_02"] = lambda: ModelCommitment(
identifier="mvimgnet_ss_02",
activations_model=get_model("mvimgnet_ss_02"),
laye... |
396605a18988ef9b4c6f77e435b0c5f384bf51886f9e2dd81c4ae16f42b2bca3 | Python | 356 | 13 | from monai.networks.nets import SwinUNETR
import torchinfo
if __name__ == '__main__':
model = SwinUNETR(
in_channels=1,
out_channels=2,
img_size=(128, 128, 128),
feature_size=48,
norm_name="instance",
use_checkpoint=True,
)
torchinfo.summary(mode... |
82cecb2875d5a3c34d7594c94c2be81f2bcd6d80d19be9b4fc91891c708d3fe1 | Python | 356 | 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['compact_cornets_V4'] = lambda: ModelCommitment(identifier='compact_cornets_V4', activations_model=get_model('compact_cornets_V4'), layer... |
a70a3ee54032babbf7521d2e26d34280b1a949446734517468f7d31d0de01ef9 | Python | 356 | 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['compact_cornets_IT'] = lambda: ModelCommitment(identifier='compact_cornets_IT', activations_model=get_model('compact_cornets_IT'), layer... |
aef2b2189dc09c438ddb9927510c826bbd1590a8e78ce5ad063e2fc3ca07a300 | Python | 356 | 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['bp_resnet50_julios'] = lambda: ModelCommitment(identifier='bp_resnet50_julios', activations_model=get_model('bp_resnet50_julios'), layer... |
db5c3fe52f4ed3bdf1344a7a46b0d16d402b3b99edea39af678d8cbe912491b5 | Python | 356 | 16 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 20 17:32:25 2024
@author: costantino_ai
"""
import pytest
from brainscore_vision import load_dataset, load_stimulus_set
@pytest.mark.private_access
def test_existence():
assert load_dataset('Bracci2019') is not None
assert load_stimulus_set... |
a8524e20b1704b61ff95c8db42ec03ba6a1f785116bfe870d1d9c675d4641f04 | Python | 357 | 11 | def pytest_configure(config):
"""Configure logging before any tests run"""
import logging
import sys
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
handlers=[logging.StreamHandler(sys.stdout)],
force=True, # Overr... |
bd8d27bd3e2f3dc149d992567fdc1e61255e24d23bfe2061d55cd3a59dab5823 | Python | 358 | 11 | import numpy as np
from numpy.random import RandomState
from brainscore_core.supported_data_standards.brainio.assemblies import DataAssembly
from brainscore_vision import load_metric
def test_fifty_percent():
metric = load_metric('accuracy')
accuracy = metric(np.array([5, 3, 6, 7, 1, 8]), np.array([5, 2, 1, ... |
d5e753276e7e1ca82c9f1dbcbf7b591b331261d263704b17b3a3eecb3013b14c | Python | 358 | 12 | from argclz.commands import parse_command_args
from .main_lick_score import LickScoreOptions
from .main_licking_cmp import LickingCmpOptions
parse_command_args(
usage='python -m rscvp.track CMD...',
description='post processing of tracking data using DeepLabCut',
parsers=dict(
lc=LickingCmpOptions,... |
0790594556cbf552dd985dad8cccb3dea02e65ce2f4e69a120eec9fe6afb304a | Python | 359 | 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["CE-MMCR_lmda_03"] = lambda: ModelCommitment(
identifier="CE-MMCR_lmda_03",
activations_model=get_model("CE-MMCR_lmda_03"),
l... |
0fbbd6cb6229ecf56629a710c2a0f0efd3f941ed4fec0cb855a84c4527f24bfc | Python | 359 | 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["eMMCR_lmda_01_3"] = lambda: ModelCommitment(
identifier="eMMCR_lmda_01_3",
activations_model=get_model("eMMCR_lmda_01_3"),
l... |
1e343c5c7516771eea6f51329ee26efb20ae869c3c96c59e8e8e1b32cc0d65e1 | Python | 359 | 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["eBarlow_Vanilla"] = lambda: ModelCommitment(
identifier="eBarlow_Vanilla",
activations_model=get_model("eBarlow_Vanilla"),
l... |
259748bed10886b0cc96b1ad45de9e109533238c0d92d4563165457c71a8d0ff | Python | 359 | 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["CE-MMCR_lmda_04"] = lambda: ModelCommitment(
identifier="CE-MMCR_lmda_04",
activations_model=get_model("CE-MMCR_lmda_04"),
l... |
31b8df4dd21ba8574efdda6a218733f1e915c6c2e67431d2c1667a7643febd67 | Python | 359 | 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["eBarlow_lmda_01"] = lambda: ModelCommitment(
identifier="eBarlow_lmda_01",
activations_model=get_model("eBarlow_lmda_01"),
l... |
37303127a36b3f903cf3aef30cd42349d29cc4d2d04c85d305df4a608b1bec9f | Python | 359 | 10 | from .Alpha_nodes import AlphaW_Node, AlphaZ_Node
from .Tau_nodes import TauD_Node
from .Theta_nodes import ThetaW_Node, ThetaZ_Node
from .W_nodes import W_Node, SW_Node
from .Y_nodes import Y_Node
from .Z_nodes import Z_Node, SZ_Node
from .multiview_nodes import *
from .basic_nodes import *
from .nongaussian_nodes imp... |
463cf4617ab9e3982b079ddb4d26d5c5c5123b0f84c7616fb143b248528251ff | Python | 359 | 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["eMMCR_lmda_01_1"] = lambda: ModelCommitment(
identifier="eMMCR_lmda_01_1",
activations_model=get_model("eMMCR_lmda_01_1"),
l... |
4ce5275d480a2242003cfec5c5ba221a7f1674d077d5df2261336eabe5492b1c | Python | 359 | 14 | import numpy as np
low_bounds=[]
with open('unicode.txt','r') as f:
lines=f.readlines()
for line in lines:
try:
line=line[:line.find('：')]
a,_=line.split('-')
low_bounds.append(int(a,16))
except:
a,_=line.split('–')
#print()
np.save('u... |
5a5d4f7aeadcd3d5a185a703062b0681bdff9937d6aceb24c22fa5e94d4e2fb8 | Python | 359 | 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["eMMCR_VanillaV2"] = lambda: ModelCommitment(
identifier="eMMCR_VanillaV2",
activations_model=get_model("eMMCR_VanillaV2"),
l... |
5c9b2f17f8501cdd5515db032d0437b033051583eb5a9fbf848dc171a669799f | Python | 359 | 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["eMMCR_lmda_01_2"] = lambda: ModelCommitment(
identifier="eMMCR_lmda_01_2",
activations_model=get_model("eMMCR_lmda_01_2"),
l... |
8d92654bc8924b7fc05c12e0870b1e865e0dfb52c56a358a292644e549d5e3ba | Python | 359 | 11 | """ Imputations for cross-sectional data. """
from .random import random
from .central_tendency import mean
from .central_tendency import mode
from .central_tendency import median
from .buck_iterative import buck_iterative
from .em import em
from .fast_knn import fast_knn
__all__ = ["random", "mean", "mode", "median... |
907fa20cecd7103d97e060df2b543a4cd5650cafb8a3d427456e0b0537ab4358 | Python | 359 | 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["CE-MMCR_lmda_01"] = lambda: ModelCommitment(
identifier="CE-MMCR_lmda_01",
activations_model=get_model("CE-MMCR_lmda_01"),
l... |
9150d58571a69ffe7b21553c62e26769eabd2b8593d57521c6a9a598174eb07c | Python | 359 | 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["eMMCR_Vanilla_2"] = lambda: ModelCommitment(
identifier="eMMCR_Vanilla_2",
activations_model=get_model("eMMCR_Vanilla_2"),
l... |
92c8fd0e8bf7eb6c10d30e4f1f7073d4ee97dd55663ef5ee4b1b76356bf6f6ef | Python | 359 | 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["eMMCR_Vanilla_1"] = lambda: ModelCommitment(
identifier="eMMCR_Vanilla_1",
activations_model=get_model("eMMCR_Vanilla_1"),
l... |
b4b116a2980994fe2b053addc14463cf3d54262fd023ae8aebd43ba5ad16c948 | Python | 359 | 8 | from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from brainscore_vision import model_registry
from .model import get_layers,get_model
model_registry['convnext_tiny_sup'] = \
lambda: ModelCommitment(identifier='convnext_tiny_sup', activations_model=get_model('convnext_tiny_sup'), la... |
bb664f32b621ce83eafeee80685947a8f634b1a5267743bf935159d156426637 | Python | 359 | 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["CE-MMCR_lmda_00"] = lambda: ModelCommitment(
identifier="CE-MMCR_lmda_00",
activations_model=get_model("CE-MMCR_lmda_00"),
l... |
c25799c0e5e77293573476504b422c6a023bb36cf0b45c3e49a2a248dd6ae302 | Python | 359 | 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["CE-MMCR_lmda_02"] = lambda: ModelCommitment(
identifier="CE-MMCR_lmda_02",
activations_model=get_model("CE-MMCR_lmda_02"),
l... |
cd5e98a46b29ad05853c9018632848c026d2b6641bbee2814c233ca2e262ff7e | Python | 359 | 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["eMMCR_lmda_01V2"] = lambda: ModelCommitment(
identifier="eMMCR_lmda_01V2",
activations_model=get_model("eMMCR_lmda_01V2"),
l... |
e27c19713a6d7296deb6e59f7a21c06af82cf25465932716e194b61ef588f581 | Python | 359 | 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["CE-MMCR_lmda_05"] = lambda: ModelCommitment(
identifier="CE-MMCR_lmda_05",
activations_model=get_model("CE-MMCR_lmda_05"),
l... |
1d4f0135f806ee92f2ab965a2a68b95544b1102e03bd7445fbc6b961200a3f03 | Python | 360 | 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['dbp_resnet50_julios'] = lambda: ModelCommitment(identifier='dbp_resnet50_julios', activations_model=get_model('dbp_resnet50_julios'), la... |
20a95c7280806d42d92d23fccf397bf636fbb3152bcd1b265f2a481fb9fc0952 | Python | 360 | 7 | from .sequence_tagger_model import SequenceTagger, FastSequenceTagger
from .dependency_model import SemanticDependencyParser
from .language_model import LanguageModel
from .text_classification_model import TextClassifier
from .controller import EmbedController
from .dependency_model_1 import SemanticDependencyParser1
f... |
3236b4c58ee35d0b72f0d52b1d6450452c33b6e08ad290a141f0404e234913c1 | Python | 360 | 15 | import requests
def send_request(url: str, payload: list[dict]) -> list[dict]:
response = requests.post(url, json=payload)
response.raise_for_status()
return response.json()
payload = [
{"imdb_output": "This movie was silly, I have to say "},
]
response = send_request(url="http://localhost:8000/pre... |
38faf90b01a57df21521b6e38fe853bf5a470f83b2a66b7c3936cc4505bd3fcb | Python | 360 | 16 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 20 17:32:25 2024
@author: costantino_ai
"""
import pytest
from brainscore_vision import load_dataset, load_stimulus_set
@pytest.mark.private_access
def test_existence():
assert load_stimulus_set('Maniquet2024') is not None
assert load_datas... |
4628fbad250831de2acd76ee530a126b21b878d695c213f75dda825dadaee0c9 | Python | 360 | 10 | import torch
from .large_margin_softmax_loss import LargeMarginSoftmaxLoss
class SphereFaceLoss(LargeMarginSoftmaxLoss):
# implementation of https://arxiv.org/pdf/1704.08063.pdf
def scale_logits(self, logits, embeddings):
embedding_norms = torch.norm(embeddings, p=2, dim=1)
return logits * em... |
921f903d5ecd16a0b7fb4b908393b7621d7ac2b87f0c0e5f159744d0dcf5e3e1 | Python | 360 | 12 | #!/usr/bin/env python
"""
# Author: Tianqi wang, Huitong zhu
# File Name: __init__.py
# Description:
"""
__author__ = "Tianqi wang, Huitong zhu"
__email__ = "tqwang743@163.com, zht_0518@163.com"
from .utils import clustering
from .preprocess import preprocess_adj, preprocess, construct_interaction, add_contrastive_la... |
98b39943bd4439726104aec374b8178b7a145d372ef074d3ad5b5e354cc8d910 | Python | 360 | 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['compact_resnet50_IT'] = lambda: ModelCommitment(identifier='compact_resnet50_IT', activations_model=get_model('compact_resnet50_IT'), la... |
9f576c85eaba6fdf376df68fbbf366599b1827cc2cf7598d0b269246a7a04f54 | Python | 360 | 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['compact_resnet50_V4'] = lambda: ModelCommitment(identifier='compact_resnet50_V4', activations_model=get_model('compact_resnet50_V4'), la... |
bb16e653cc2445e415a117de5aed4ec8e135e2b46c49b25e20ba86163884cb9d | Python | 360 | 13 | #!/usr/bin/env python
"""
# Author: Kangning Dong
# File Name: __init__.py
# Description:
"""
__author__ = "Kangning Dong"
__email__ = "dongkangning16@mails.ucas.ac.cn"
from .STAGATE import STAGATE
from .Train_STAGATE import train_STAGATE
from .utils import Transfer_pytorch_Data, Cal_Spatial_Net, Stats_Spatial_Net, m... |
01941e76de1b72ec1529d3055a37cfafe3f83fb00e2ea93155c58f4916a54106 | Python | 362 | 16 | from setuptools import setup, find_packages
setup(
name='analog-nasbench',
version='0.1.3',
packages=find_packages(),
include_package_data=True,
package_data={
'analog-nasbench': ['data.anb'],
},
data_files=[('analog-nasbench', ['analognasbench/data.anb'])],
install_requires=[
... |
483f3d956e097f657610da740b22030c5124aabd63238150363984159257e9f6 | Python | 362 | 11 | from brainscore_vision import benchmark_registry
# neural alignment
from .domain_transfer_neural import Igustibagus2024_ridge
benchmark_registry['Igustibagus2024-ridge'] = Igustibagus2024_ridge
# analysis benchmarks
from .domain_transfer_analysis import OOD_AnalysisBenchmark
benchmark_registry['Igustibagus2024.IT_r... |
738f550d9cacc146e1c8f124413e72d3dd5299998925ffc3defc10924eb2bbe1 | Python | 362 | 11 | import pandas as pd
import numpy as np
# import copy
import matplotlib.pyplot as plt
def label_point(x, y, val, ax, alpha=0.5, fontsize=10):
a = pd.concat({'x': x, 'y': y, 'val': val}, axis=1)
# print(a)
for i, point in a.iterrows():
ax.text(point['x']+ ax.get_xlim()[1]*0.0, point['y'], str(point[... |
97a74b6d196004fd9af5ff87cd6fee0b771044e8bad82b78a8652651337296cf | Python | 362 | 15 | """
Classes for dealing with biological sequences. See `core` for the abstract
sequence classes and `nib` and `qdna` for specifics of various
formats.
"""
from bx.seq.core import (
infer_format,
reverse_complement,
seq_file,
seq_reader,
seq_writer,
)
__all__ = ["infer_format", "reverse_complement"... |
bee579580045081bdb960a8158771125de82bcc6a1da32338bf0ad8774be81d9 | Python | 362 | 17 |
__all__ = ["inv_norm"]
def inv_norm(pheno):
import scipy.stats as stats
if pheno is None:
raise ValueError("Expected non-null numpy vector")
if pheno.ndim > 1:
raise ValueError("Expected numpy vector")
k = 3 / 8.0
n = len(pheno)
ranks = stats.rankdata(pheno)
return stat... |
e7dcf3a72e346647adb1993ece8630a0ce8ed9d385e8ecc7645cb827bd7b1645 | Python | 362 | 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['AT_efficientnet-b2'] = \
lambda: ModelCommitment(identifier='AT_efficientnet-b2', activations_model=get_model('AT_efficientnet-b2'),... |
11f7cdf886914d7872a2c39dab6cea042c4e567ef3d6f81e0b22147973b11f6c | Python | 364 | 11 | import yaml
from types import SimpleNamespace
from typing import Union
from pathlib import Path
def load_args_from_yaml(yaml_file_path: Union[str, Path]) -> SimpleNamespace:
""" Little helper function to load the arguments from a yaml file """
with open(yaml_file_path, 'r') as file:
args = yaml.safe_l... |
52b9525a7411b5cca5fdc71eb0683560949bb37c4ba210a15a4fd3dabd610071 | Python | 364 | 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['chresmax_v3_cl_01_v2'] = lambda: ModelCommitment(identifier='chresmax_v3_cl_01_v2', activations_model=get_model('chresmax_v3_cl_01_v2'),... |
53abd218d8701446dc67a428c1f8bbe0279415d61b71fd95f041849cf66ac3b7 | Python | 364 | 14 | import pytest
from qtpy import PYQT6, PYSIDE6
def test_qtsvg():
"""Test the qtpy.QtSvg namespace"""
QtSvg = pytest.importorskip("qtpy.QtSvg")
if not (PYSIDE6 or PYQT6):
assert QtSvg.QGraphicsSvgItem is not None
assert QtSvg.QSvgWidget is not None
assert QtSvg.QSvgGenerator is not Non... |
734b00e26ac8a859953f06c6e2ec7f6c2a93714c47799e94c87af7d4e091f853 | Python | 366 | 16 | from __future__ import annotations
from PySide6 import QtWidgets
class ZoomWidget(QtWidgets.QDoubleSpinBox):
def __init__(self) -> None:
super().__init__()
self.setDecimals(1)
self.setRange(1, 1000)
self.setSuffix(" %")
self.setValue(100)
@property
def scale(self)... |
046c279dc81e94ea164417d16b5d35f45843ee818ac4a547c4b400e4d7fe86c4 | Python | 367 | 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["eSimCLR_lmda_04_1"] = lambda: ModelCommitment(
identifier="eSimCLR_lmda_04_1",
activations_model=get_model("eSimCLR_lmda_04_1"),... |
05129cf92b9eb226128a152178a428ea47785ab0d95c11950d58ffa6ad9441ca | Python | 367 | 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["eBarlow_lmda_05_1"] = lambda: ModelCommitment(
identifier="eBarlow_lmda_05_1",
activations_model=get_model("eBarlow_lmda_05_1"),... |
0ba772329454c7ba044cfb9a3c56ed9d943f7a02794923e785fc584eb8f96caf | Python | 367 | 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["eSimCLR_lmda_05_1"] = lambda: ModelCommitment(
identifier="eSimCLR_lmda_05_1",
activations_model=get_model("eSimCLR_lmda_05_1"),... |
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