sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
d2ff1bbe3d7208d05d2ce93075d27246077b70173922182833dbc6425472d309 | Python | 382 | 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['jem_imagenet_p0p8_s1'] = lambda: ModelCommitment(identifier='jem_imagenet_p0p8_s1', activations_model=get_model('jem_imagenet_p0p8_s1'),... |
d3cb094cb41fd2890269bc4901ddfb07797d9976d551fea9730af7d336fed135 | Python | 382 | 16 | '''Test the base abstract class Calibration'''
import unittest
from ogo.calibration.calibration import Calibration
import os
class TestCalibration(unittest.TestCase):
'''Test the base abstract class Calibration'''
def test_cannot_instantiate(self):
with self.assertRaises(TypeError):
c = ... |
f321e8977bbaba9914b3c68ba3e17359998cb8315a397569b61ea4b4b2a55f05 | Python | 382 | 14 | from setuptools import setup
from pip._internal.req import parse_requirements
requirements = parse_requirements('requirements.txt', session=False)
install_requires = [str(req.requirement) for req in requirements]
setup(
name='countnigra',
version='0.1.0',
author='onnonuro',
packages=['countnigra'],
... |
1f9986734d228fb196ebeef6571d8b5e2bfc19dad20ebdc7eb636d8388d8b753 | Python | 383 | 16 | #!/bin/env python
import numpy as np
import sys
inp1 = sys.argv[1]
inp2 = sys.argv[2]
#Combines the 2D PC projections to create a 3D PC projection
lines1 = np.loadtxt(inp1, dtype=float, usecols=(0,1), unpack=False)
lines2 = np.loadtxt(inp2, dtype=float, usecols=(1,), unpack=False)
n = len(lines1)
for i in range(n):
... |
a5153165130912e63b53987141e1737b7a35bfc9e7a5c065e0fb60167b231d07 | Python | 383 | 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_augself_mlp_1"] = lambda: ModelCommitment(
identifier="eBarlow_augself_mlp_1",
activations_model=get_model("eBarlow_augs... |
bf470b3f6290ef440e6d804a05ef6f7515851f0f80a33fe11b309d6a90ed20a2 | Python | 383 | 21 | import time
import sys
sys.path.append("../")
import jieba
jieba.initialize()
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'))
log_f.close()
print('cost ' + str(tm_c... |
c706a4fbf9e95fe8c1f7f96c9666e108c50aba024e8e74bc74d19059aece9f21 | Python | 383 | 15 |
from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from model import get_model, LAYERS
model_registry['blt_vs'] = lambda: ModelCommitment(
identifier='b... |
f7ef8755a34e7c8d4aeeb9ffad1a9c94bf055009e97de06b46a11021a430db13 | Python | 383 | 7 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from ultralytics.models.yolo.classify.predict import ClassificationPredictor
from ultralytics.models.yolo.classify.train import ClassificationTrainer
from ultralytics.models.yolo.classify.val import ClassificationValidator
__all__ = "ClassificationPr... |
f834ec249ef5e16bb18e46b6d5259578d189785f51456d2817ae562c06819974 | Python | 383 | 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_Mom_lmda_0001_1"] = lambda: ModelCommitment(
identifier="eMMCR_Mom_lmda_0001_1",
activations_model=get_model("eMMCR_Mom_lm... |
085908efaaebd88094cf04276e1e234a6258f6f306ab9eca8b2c0e948b21729b | Python | 384 | 6 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['alexnet_training_seed_04'] = lambda: ModelCommitment(identifier='alexnet_training_seed_04', activations_model=get_model('alexnet_trainin... |
1256eec5359759e8d9dab29211feb01dbc65f76771e29d96fa4e233f38dc8578 | Python | 384 | 6 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['alexnet_training_seed_07'] = lambda: ModelCommitment(identifier='alexnet_training_seed_07', activations_model=get_model('alexnet_trainin... |
159bced13550acc50eccbb5481fe1ac13adc106554268478b46347beb02f81a1 | Python | 384 | 6 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['alexnet_training_seed_01'] = lambda: ModelCommitment(identifier='alexnet_training_seed_01', activations_model=get_model('alexnet_trainin... |
3a33c534fda4c6c0021a5e73c39637e763041325f326991bdb528449e4b7f9b5 | Python | 384 | 6 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['alexnet_training_seed_05'] = lambda: ModelCommitment(identifier='alexnet_training_seed_05', activations_model=get_model('alexnet_trainin... |
4eaba9b22d793fce8ed893ec0d38483afff1180e13dfaa08429ade5b2b58f190 | Python | 384 | 6 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['alexnet_training_seed_08'] = lambda: ModelCommitment(identifier='alexnet_training_seed_08', activations_model=get_model('alexnet_trainin... |
6c1261da6212c930b3c6629698865a337d8d30c4baf47b714a67b45b3eedba64 | Python | 384 | 6 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['alexnet_training_seed_09'] = lambda: ModelCommitment(identifier='alexnet_training_seed_09', activations_model=get_model('alexnet_trainin... |
8a8dccd3d5318bf86701c7ccedc35da7bc3dc149cfa09cfe209eb31497fdd9e2 | Python | 384 | 6 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['alexnet_training_seed_06'] = lambda: ModelCommitment(identifier='alexnet_training_seed_06', activations_model=get_model('alexnet_trainin... |
9f887bb8c89ac77551e145449cd12fedd1e879628eb58e7141eb0a4beb8c32bd | Python | 384 | 16 | import sys, os
infile = sys.argv[1]
outfile = open(sys.argv[2], "w")
print("\t".join(["index", "cred1", "prob1"]), file=outfile)
count = 1
with open(infile, "r") as f:
for line in f:
if line.startswith("SNP"):
continue
items = line.rstrip("\n").split("\t")
print("\t".join([st... |
a09e4e5ce7a8b4136e6d96eafa3214afec31f0664346a1e2f63c58d8adcd2d8b | Python | 384 | 6 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['alexnet_training_seed_03'] = lambda: ModelCommitment(identifier='alexnet_training_seed_03', activations_model=get_model('alexnet_trainin... |
b5e2c13f69975937dabab39e6df6135bec34713347a0e51091e80096157cf4ad | Python | 384 | 17 | """# Custom text labels for widgets
An example showing how to create custom text labels for your widgets.
"""
from magicgui import magicgui
# use a different label than the default (the parameter name) in the UI
@magicgui(x={"label": "widget to set x"})
def example(x=1, y="hi"):
"""Example function."""
retu... |
d81b3c89e6b61ae1b0c3fd5173dec67542541d621486e7e302397ad6d5f9fa37 | Python | 384 | 6 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['alexnet_training_seed_10'] = lambda: ModelCommitment(identifier='alexnet_training_seed_10', activations_model=get_model('alexnet_trainin... |
ed1587cfa5391b9432039c058785a5ac176997fc86365bd8a9606e66a519715a | Python | 384 | 14 | # cec2017.functions
# Author: Duncan Tilley
# Combines simple, hybrid and composition functions (f1 - f30) into a single
# module
from .simple import *
from .hybrid import *
from .composition import *
all_functions = [
f1, f2, f3, f4, f5, f6, f7, f8, f9, f10,
f11, f12, f13, f14, f15, f16, f17, f18, f... |
f478a57eb298d6a70229b92ce50da8277528fa693738e1d366817f2554f38e3b | Python | 384 | 6 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['alexnet_training_seed_02'] = lambda: ModelCommitment(identifier='alexnet_training_seed_02', activations_model=get_model('alexnet_trainin... |
47f6363b86c0bfaa7a6abf723975136d17fae4a5f002d2814a5cce8834d18980 | Python | 385 | 11 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from .sam import Sam
from .image_encoder import ImageEncoderViT
from .mask_decoder import MaskDecoder
from .prompt_encoder... |
5f38464ad920a87c248157e1368b86e5bd969afe668212afdc3b3bb17bd37a93 | Python | 385 | 18 | # Generated by Django 4.2.26 on 2026-01-08 08:39
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("engine", "0095_fix_related_names"),
]
operations = [
migrations.AddField(
model_name="labeledshape",
name="score",
... |
7f616020d73fadf61e9235c3994cbe6a22943e883a83688e6c6a6bf23b2fda95 | Python | 385 | 14 | import pytest
from tests.utils import pytest_importorskip
def test_shiboken():
"""Test the qtpy.shiboken namespace"""
shiboken = pytest_importorskip("qtpy.shiboken")
assert shiboken.isValid is not None
assert shiboken.wrapInstance is not None
assert shiboken.getCppPointer is not None
assert ... |
8ff9713c3438daeb7893c2f78e3351983f8465ef9912c4a68a56bb8ac4e733fa | Python | 385 | 16 | from rscvp.util.util_demo import run_demo
from rscvp.visual.main_reliability import VisualReliabilityOptions
class ExampleRun(VisualReliabilityOptions):
exp_date = '210315'
animal_id = 'YW006'
plane_index = 0
neuron_id = 2
signal_type = 'df_f'
use_default = True
debug_mode = True
if __na... |
9d917b308b2c69719e58d0cc2034087ea9366f354857ffe25e23b4d7f5af90c0 | Python | 385 | 14 |
def registration_widget():
# todo probe_track in sharpy_track
# todo think about solution to check and load atlas data
@magicgui(
layout='vertical',
call_button='start registration GUI'
)
def widget(
viewer: Viewer
) -> None:
reg_viewer = Registrat... |
dcea3540aaa6ee084906caec50117de7c2158dcf3fa7e147a71b4c2d4764bde4 | Python | 385 | 17 | # Generated by Django 4.2.1 on 2023-09-27 06:52
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("organizations", "0001_initial"),
]
operations = [
migrations.AddField(
model_name="invitation",
name="sent_date",
... |
1fb8931b186ba13a73b2cfc3bae2980eaff8b248827f6739f22d7aaf116e3486 | Python | 386 | 4 | """A sub-package that contains the abstract base class for all processors, :py:class:`~corelay.processor.base.Processor`. Processors perform a
specific operation in a :py:class:`~corelay.pipeline.base.Pipeline` that can be parameterized using instances of :py:class:`~corelay.base.Param`.
Furthermore, the sub-package co... |
36dc1fd7a57ad3f199f03228a68f23641cdbfcb088c48d72c6bf67789d5d71d3 | Python | 386 | 24 |
import os
import glob
import nibabel as nib
import numpy as np
import scipy.io
from pathlib import Path
'''
prepare training dataset.
'''
# input_fn = Path('/testing/testing_lp_cnn/dipole.mat')
# output_fn = Path('/testing/testing_lp_cnn/dipole.npy')
dipole = scipy.io.loadmat(input_fn)['C']
dipole = np.swapaxes(... |
5b4bfc45fa6c81cb71b50546252423176c65e39164b41efecacb9377d0a57754 | Python | 386 | 16 | """Define our own exceptions to be able to filter them out easily"""
__all__ = ['BreadException', 'BreadWarning']
class BreadException(UserWarning):
# inherit from UserWarning, which itself is a subclass of Exception
# so we can write
# try:
# ...
# raise LineageException(...)
# except LineageException as e:
... |
7ba762fc3c3b27dc28ffdde7150983470b302e136f4b14b74069fbb96301e75a | Python | 386 | 15 | import sys
from training import training
from inference import inference
from evaluation import evaluate
from aggregate import aggregate
from hydra import compose, initialize
if __name__ == '__main__':
initialize(version_base=None, config_path="configs/")
cfg = compose(config_name="all")
#training(cfg)
... |
a878eb69627711b1381eccc9f4bbb0fe65a0209db818728ae919ebf1a8000591 | Python | 386 | 13 | import json
from tqdm import tqdm
user = json.load(open("/data2/whr/czl/TwiBot22-baselines/datasets/Twibot-22/user.json", "r"))
id_tweet = json.load(open("/data2/whr/czl/TwiBot22-baselines/src/twibot22_Botrgcn_feature/id_tweet.json", "r"))
ret = {}
for u, t in tqdm(zip(user, id_tweet.values())):
ret[u["id"]] = t
... |
47df5e7e7f30414ba925a7f1d92fc3051ca7c73d7ceebfc0e01f1d34e6e63091 | Python | 387 | 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_02_1000ep"] = lambda: ModelCommitment(
identifier="eBarlow_lmda_02_1000ep",
activations_model=get_model("eBarlow_lm... |
71cfdd76a125dd6ee6a990f466059361383e947f66acc74b8201b24326692a26 | Python | 387 | 11 | # __init__.py - 모델 등록
from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
# hk_model_1 등록
model_registry['hk_model_1'] = lambda: ModelCommitment(
identifier='hk_model_1',
activations_model=get_mod... |
7779995e520e199db8f2c4cd123b90bf4b6f92d484c70aaafcd2bf5f312f0022 | Python | 387 | 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_02_1_full"] = lambda: ModelCommitment(
identifier="eBarlow_lmda_02_1_full",
activations_model=get_model("eBarlow_lm... |
9b63873e05a6357a191af60f359b306e2b6e901ced77712e861fae2110856dc2 | Python | 387 | 16 | from __future__ import absolute_import
from .balanced_bce import *
from .bitempered_loss import *
from .dice import *
from .focal import *
from .focal_cosine import *
from .functional import *
from .jaccard import *
from .joint_loss import *
from .lovasz import *
from .soft_bce import *
from .soft_ce import *
from .so... |
c0e374cff56abdd10e151566d47cfbc3ad593a500526fec6c8469359dd9f360d | Python | 387 | 17 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 24 17:22:59 2024
@author: costantino_ai
"""
import pytest
from brainscore_vision import load_benchmark
@pytest.mark.parametrize('benchmark', [
'Maniquet2024-confusion_similarity',
'Maniquet2024-tasks_consistency',
])
def test_benchmark_reg... |
d57023aea53ffa0043d25402e376e31f9f39039fbabc265d38278f7d4b1e0e26 | Python | 387 | 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_1_full"] = lambda: ModelCommitment(
identifier="eBarlow_Vanilla_1_full",
activations_model=get_model("eBarlow_Va... |
218603f4d1423c6bf592391e25033b5ff2562557bd3222e328270610aa786730 | Python | 388 | 15 | def clear_layout(layout):
while layout.count():
item = layout.takeAt(0)
widget = item.widget()
if widget is not None:
widget.setParent(None)
widget.deleteLater()
else:
del item
def reset_table(table):
model = table.model()
model.state_chan... |
2decd77ee7cc0120440bd052d63300d7d2360bcb884a389f4710e060428292df | Python | 388 | 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_robust_V4'] = lambda: ModelCommitment(identifier='compact_resnet50_robust_V4', activations_model=get_model('compact_res... |
35813251f658b9969ed8e7f66898dab982de9257469389de66597473452d82b2 | Python | 388 | 18 | from datetime import datetime
project = "CellAgentChat"
author = "Vishvak Raghavan"
copyright = f"{datetime.now():%Y}, {author}"
release = "0.1.0"
extensions = [
"myst_parser",
]
templates_path = ["_templates"]
exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
html_theme = "sphinx_rtd_theme"
html_static_p... |
da52381b25b25aaa3b18d5235ba77b28a3c5a9747f4684ef2e61780c5bbdfdfe | Python | 388 | 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_robust_IT'] = lambda: ModelCommitment(identifier='compact_resnet50_robust_IT', activations_model=get_model('compact_res... |
05d40f299395d69541bcc59b89bd1bf21fdc622933daf36925a8dc76ed97e1c0 | Python | 389 | 6 |
from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['alexnet_z_axis_iteration=4'] = lambda: ModelCommitment(identifier='alexnet_z_axis_iteration=4', activations_model=get_model('alexnet_z_... |
54bb1512721d21739971d263627ea8ec26b9afc51421cf7004519f6f3b03aeee | Python | 389 | 6 |
from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['alexnet_z_axis_iteration=1'] = lambda: ModelCommitment(identifier='alexnet_z_axis_iteration=1', activations_model=get_model('alexnet_z_... |
596ba80e4cd053f762142817fd1fe4c534d2bbdf0e28005b05d0e8c4241931a9 | Python | 389 | 6 |
from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['alexnet_z_axis_iteration=2'] = lambda: ModelCommitment(identifier='alexnet_z_axis_iteration=2', activations_model=get_model('alexnet_z_... |
bedd73367180199e8f23238db3913531de29ff4d7d8427167885f4579cc73e3f | Python | 389 | 6 |
from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['alexnet_z_axis_iteration=3'] = lambda: ModelCommitment(identifier='alexnet_z_axis_iteration=3', activations_model=get_model('alexnet_z_... |
a7a86b9c9f2f3c045a57b68bbd5c02608039272879e6c406170de3922c26f949 | Python | 390 | 18 | import pyvisa as visa
import pyvisascope
import logging
visa.log_to_screen('INFO')
logger = logging.getLogger('pyvisa')
log2file = logging.FileHandler('experiment.log')
logger.addHandler(log2file)
def main():
# list the connected devices
print("Devices connected to your computer:")
print(pyv... |
c6c676e72ce09093fcfb8540fa969ba3379dd7b5d62b87ec66fe7d54c87ef9fc | Python | 391 | 8 | from .descriptor import MeanAbsFoveatedGaborExtractor
def test_feature_count():
extractor = MeanAbsFoveatedGaborExtractor()
n_filters = len(extractor.config.spatial_frequencies) * extractor.config.n_orientations
n_positions = len(range(0, extractor.config.image_size - extractor.mid_window + 1, extractor.b... |
c7ba803c3ac0d192639d6c7dafcd43249f722668e7c1c16e0299f5c5b3d0b91e | Python | 391 | 21 | from .models import (
RANGESchNet,
StandardRANGESchNet,
RANGEPaiNN,
StandardRANGEPaiNN,
RANGESo3krates,
StandardRANGESo3krates,
RANGEMACE,
StandardRANGEMACE,
)
__all__ = [
"RANGESchNet",
"StandardRANGESchNet",
"RANGEPaiNN",
"StandardRANGEPaiNN",
"RANGESo3krates",
... |
7f848ca569a50515ac50062ec7ac5cd0cafefc9318efc46b3029fbe0dead867f | Python | 392 | 17 | import numpy as np
from impyute.ops import matrix
def _is_gt_5(x):
return x > 5
def test_map_nd_2d():
arr = np.arange(10).reshape([5, 2])
expected = np.array([
[False, False],
[False, False],
[False, False],
[True, True],
[True, True],
])
actual = matrix.map... |
feee03178a185479624b3560ccbd9aec978aa452459f41762136082180477ba1 | Python | 392 | 17 | import numpy as np
import impedancefitter as ifit
Rw = 1.00
gamma = 0.8
wd = 1
freq = np.logspace(-3, 4, num=100)
omega = 2.0 * np.pi * freq
models = ["Ws", "ADIaA", "ADIbA", "ADIIA"]
for model in models:
ecm = ifit.get_equivalent_circuit_model(model)
Z = ecm.eval(omega=omega, Rw=Rw, gamma=gamma, wd=wd)
... |
6f39011d17ccfd979257f3e81edb08b5b86300696c8ba983dbcfac4fb0937bb2 | Python | 393 | 14 | """Collects commonly-used core classes & functions for easier import.
Ex: `from nems import Model`
"""
import nems.registry
from nems.models.base import Model, Model_List
from nems.models.LN import LN_STRF, LN_pop
from nems.models.CNN import CNN_pop
from nems.models.decoder import binary
from nems.tools.demo_data.f... |
c41455547115d238476ebeb23083104ea1926f1588d9f94c73502b905feb7c1f | Python | 393 | 30 | import tensorflow as tf
import numpy as np
import torch
# Backends
PYTORCH = 'torch'
TFKERAS = 'tf'
NUMPY = 'numpy'
# Types (for memory)
NP_COMPLEX = np.complex128
NP_FLOAT = np.float64
TF_COMPLEX = tf.complex64
TF_FLOAT = tf.float32
T_FLOAT = torch.double
T_COMPLEX = torch.cdouble
# Test seed
TEST_SEED = 31415... |
432aeb1a6263b06e47773e84a0df852af5ac12f24bdd5fdae4f2ed616e69f842 | Python | 394 | 11 | from dtiplayground.dmri.common.tools.base import ExternalToolWrapper
class CropDTI(ExternalToolWrapper):
def __init__(self,binary_path= None, **kwargs):
super().__init__(binary_path, **kwargs)
def crop(self,inputfile,outputfile,size: str):
arguments=[inputfile,'-o',outputfile,'-size',size]
... |
5b73f01f40b659c959171b0bca25af6a00ef78cf67fd54509c8864562519d352 | Python | 394 | 9 | from sklearn.model_selection import GridSearchCV as skGridSearchCV
from sklearn.model_selection import RandomizedSearchCV as skRandomizedSearchCV
from .search import causalize_searcher
from .split import TreatmentOutcomeStratifiedKFold
from .split import TreatmentStratifiedKFold
GridSearchCV = causalize_searcher(skGr... |
676edb79f778dd2ac2fd5af874d4c60857be12ccea8ecea45d397f2240c1ec21 | Python | 394 | 18 | # Generated by Django 3.2.15 on 2022-08-10 08:45
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("engine", "0058_auto_20220809_1236"),
]
operations = [
migrations.AddField(
model_name="labeledshape",
name="outside... |
1b8fb79b00972ff43f286c8d44ae6c14204d726f4c6f8333d9736baca4341ac0 | Python | 395 | 17 | from rscvp.util.util_demo import run_demo
from rscvp.visual.main_tuning import VisualTuningOptions
class ExampleRun(VisualTuningOptions):
exp_date = '210315'
animal_id = 'YW006'
plane_index = 0
neuron_id = 2
signal_type = 'df_f'
invalid_cache = True
use_default = True
debug_mode = True... |
5808e0a8a59ee5707587b7c5554ab3c44d513b7deb66aecc2ebf553a4e48de2c | Python | 395 | 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_augself_linear_1"] = lambda: ModelCommitment(
identifier="eBarlow_augself_linear_1",
activations_model=get_model("eBarlo... |
a101848f5259d297500daec20b02a133ce9a6c7c8d7a96bbaf13cc38c10bbece | Python | 395 | 20 | """# Multiple selection widget
A selection widget allowing multiple selections by the user.
"""
from magicgui import magicgui
@magicgui(
pick_some={
"choices": ("first", "second", "third", "fourth"),
"allow_multiple": True,
}
)
def my_widget(pick_some=("first")):
"""Dropdown selection fu... |
bb816013c97561b3453b5f5f9582460e34a071c8c7c176ccda0981e31e6462e0 | Python | 395 | 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_02_200_full"] = lambda: ModelCommitment(
identifier="eBarlow_lmda_02_200_full",
activations_model=get_model("eBarlo... |
c60f2f4e8bdcc156a17e81925e6e4e13000685e5fbb78cc010074c270ede12b1 | Python | 395 | 15 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from cvat_sdk.core.auth import (
AuthStore,
ClientAuthParameters,
ProfileEntry,
configure_client_auth_arguments,
get_auth_store_path,
make_client_from_cli,
make_client_from_profile,
)
from cvat_sdk.core.client import Clien... |
db1061a8d392984796fac050be5dd354909c7f661b9a4afc1d7364f71ad0d89d | Python | 395 | 21 | """ impyute.deletion.complete_case """
import numpy as np
from impyute.ops import wrapper
@wrapper.wrappers
@wrapper.checks
def complete_case(data):
""" Return only data rows with all columns
Parameters
----------
data: numpy.ndarray
Data to impute.
Returns
-------
numpy.ndarray
... |
f78b033b955db772e123cd81645d20fb403f7f388b8ed10a7709e36682358c3a | Python | 395 | 17 | import numpy as np
import impedancefitter as ifit
Rw = 1.00
gamma = 0.8
wd = 1
freq = np.logspace(-0.75, 3, num=100)
omega = 2.0 * np.pi * freq
models = ["Wo", "ADIaR", "ADIbR", "ADIIR"]
for model in models:
ecm = ifit.get_equivalent_circuit_model(model)
Z = ecm.eval(omega=omega, Rw=Rw, gamma=gamma, wd=wd)... |
8311ae69441c8fb905b6873e6b01f8d170b5b964c54a515b7b95f4c415c84791 | Python | 397 | 6 |
from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['alexnet_original_iteration=3'] = lambda: ModelCommitment(identifier='alexnet_original_iteration=3', activations_model=get_model('alexne... |
a1f928fd90f3cee605ca37bf1701bf98ed0dacb06926223ff21080313d94b712 | Python | 397 | 19 | """Evaluation modules for DHG-LGB framework."""
from .metrics import (
compute_metrics,
compute_confidence_interval,
print_metrics_summary,
plot_roc_curve,
plot_pr_curve,
statistical_comparison,
)
__all__ = [
'compute_metrics',
'compute_confidence_interval',
'print_metrics_summary'... |
138657afa62b7dc9c03d105269baef5da7a81148b56211b106ea768c3dc133f8 | Python | 398 | 18 | from .analysis import get_iso, run_iso_analysis
from .io import load_eeg_data, load_custom_montage
from .visualization import (
plot_iso_single_subject_save,
plot_iso_topomap,
plot_group_iso_ci
)
__all__ = [
"get_iso",
"run_iso_analysis",
"load_eeg_data",
"load_custom_montage",
"plot... |
4cebb78e2a37ede956dcb8fc302b628c9d359065607c0e77c29ef05efd9d680d | Python | 398 | 12 | """The only production construction site for ``ChatGoogleGenerativeAI``."""
from __future__ import annotations
from typing import Any
from langchain_google_genai import ChatGoogleGenerativeAI
def build(client_kwargs: dict[str, Any]) -> ChatGoogleGenerativeAI:
"""Construct a ``ChatGoogleGenerativeAI`` from prep... |
55d2fd4438368587df58ef1983ff1cdb401bcd8f2f0061c232f9b64a80323d10 | Python | 398 | 14 | from setuptools import setup, find_packages
setup(
name="euler_gpu",
version="0.1.1",
packages=find_packages(),
install_requires=["numpy", "torch"],
author="Adam Atanas",
author_email="adamatanas@gmail.com",
description="Pytorch-based implementation of Euler registration using GPU accelerat... |
f05edf18cb4d5abe9d6b4594a416b12c7231b99e67cb16623f7468e81b85860a | Python | 398 | 20 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from ultralytics.models.yolo import classify, depth, detect, obb, pose, segment, semantic, world, yoloe
from .model import YOLO, YOLOE, YOLOWorld
__all__ = (
"YOLO",
"YOLOE",
"YOLOWorld",
"classify",
"depth",
"detect",
"o... |
67e09bd14cc78eabc69bd128e3864d78c63b46e053fc8dd1a7e22f514e34ae54 | Python | 400 | 13 | from Bio import SeqIO
def init(gfile):
global hg19
hg19 = ['' for i in range(24)]
for o in SeqIO.parse(open(gfile), 'fasta'):
if o.name.replace('chr', '') not in list(map(str, range(1,23))) + ['X']:
continue
if o.name == 'chrX':
temp_key = 23
else:
... |
3579128e25e62bea0b635d53bb98de087cd7155668511a2a64456b056c83e9c3 | Python | 401 | 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['Hojin_Jang_ResNet50_Standard'] = lambda: ModelCommitment(identifier='Hojin_Jang_ResNet50_Standard', activations_model=get_model('Hoj... |
45489e89b7edc79e4b2568e4d79ff9100b2f28adb10931c67eb155a26e576160 | Python | 402 | 16 | import pytest
from qtpy import PYSIDE2, PYSIDE6
from tests.utils import pytest_importorskip
@pytest.mark.skipif(
not (PYSIDE2 or PYSIDE6),
reason="Only available by default in PySide",
)
def test_qtcharts():
"""Test the qtpy.QtCharts namespace"""
QtCharts = pytest_importorskip("qtpy.QtCharts")
a... |
8a65795e5e553493939fd3a1ae701207ff7d158d7d897295b7c5edce27404a0b | Python | 402 | 19 | # coding: utf-8
from multiprocessing import Pool
from tqdm import tqdm
def run_multi_process(f, l: list, n_processes=40):
n_processes = min(n_processes, len(l))
print(n_processes)
results = list()
pool = Pool(processes=n_processes)
for r in tqdm(pool.imap_unordered(f, l), total=len(l), ncols=75)... |
26b2db78e6543d665f0b493d982abb7b4b73954b3fbb57671ba82d4416a8ff0a | Python | 403 | 14 | import os, json
from CommandList import *
Dir = '/home/dbarriere/Recherche/Equine_Project'
subjectJsonFileName = os.path.join( Dir,
'subjects.json' )
taskJsonFileName = os.path.join( Dir,
'TaskJsonFileName.json' )
verbose = 1
runPipeline( Dir,
... |
8532d99e81f0189d9a46890d1577811544ccb23674d9cc389fc9716941e4dd24 | Python | 403 | 21 | from __future__ import print_function
import sys
import time
sys.path.append("../")
import jieba
jieba.initialize()
import jieba.posseg as pseg
url = sys.argv[1]
content = open(url,"rb").read()
t1 = time.time()
words = list(pseg.cut(content))
t2 = time.time()
tm_cost = t2-t1
log_f = open("1.log","w")
log_f.write(' /... |
afa75fe29754b06540ecf8bcdffc957260116c55c435a42c3b50c3702c9ca382 | Python | 403 | 19 | import pandas as pd
split = pd.read_csv('/data2/whr/czl/TwiBot22-baselines/datasets/Twibot-20/split.csv')
user = pd.read_csv('./feature.csv')
user_list = list(user['id'])
del_list = []
for i,r in split.iterrows():
if r[0] not in user_list:
del_list.append(i)
print(len(del_list))
split.drop(index = del... |
e01a3fcdc75baaa1c262f186560e8c0a5a6d5fd1aafa0d72a7cc576c83653774 | Python | 403 | 8 | from .descriptor import FoveatedGaborFilterEntropyExtractor
def test_feature_count():
extractor = FoveatedGaborFilterEntropyExtractor()
n_filters = len(extractor.config.spatial_frequencies) * extractor.config.n_orientations
n_positions = len(range(0, extractor.config.image_size - extractor.mid_window + 1,... |
2e6000f8ba952e5e2337692c70662c356a6a7843d8e52e04ef77a7ddd367ab31 | Python | 404 | 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['convnext_base_fb_in22k_ft_in1k'] = lambda: ModelCommitment(identifier='convnext_base_fb_in22k_ft_in1k', activations_model=get_model('con... |
66a1ac71b83e96363fc32517a78e215e8a5775a5cc445b864da2eaf4bb98df98 | Python | 404 | 14 | from .batch_easy_hard_miner import BatchEasyHardMiner
class BatchHardMiner(BatchEasyHardMiner):
def __init__(self, **kwargs):
super().__init__(
pos_strategy=BatchEasyHardMiner.HARD,
neg_strategy=BatchEasyHardMiner.HARD,
**kwargs
)
def mine(self, *args, **kw... |
bc641431d00b7b301fd20494f5abf47d25cda4b2af71ed00e2cd1fce23ba869a | Python | 404 | 16 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from django.urls import path
from django.urls.conf import include
from rest_framework import routers
from .views import AccessTokensViewSet
router = routers.DefaultRouter(trailing_slash=False)
router.register("access_tokens", AccessTokensViewSet, b... |
d59698e9eb07bae8759dc8451fec7627ae28a1139d7e5425d9b5eaf510da129b | Python | 404 | 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['deit3_base_patch16_224_fb_in1k'] = lambda: ModelCommitment(identifier='deit3_base_patch16_224_fb_in1k', activations_model=get_model('dei... |
11e6df1669b95fe695393347574813b38dc8fceab6dcb5bc1779f164046643c7 | Python | 405 | 15 |
import bpy
outputFile = 'D:/Dropbox/ACPProjects/PtNRElectrodes/Neuropixel/Pt03Range2Part1.csv'
####verts = [ bpy.context.object.matrix_world * v.co for v in bpy.context.object.data.vertices ]
verts = [ v.co for v in bpy.context.object.data.vertices ]
csvLines = [ ";".join([ str(v) for v in co ]) + "\n" ... |
25a25045d5a7b25b0e2385f6eace49d937478943eb690ce231c06c912d4e1e6e | Python | 405 | 6 |
from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['alexnet_wo_shading_iteration=4'] = lambda: ModelCommitment(identifier='alexnet_wo_shading_iteration=4', activations_model=get_model('al... |
2d098c8d4ed521b529839dd9a728a7f204f50eca67673691352476f9021a8966 | Python | 405 | 6 |
from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['alexnet_wo_shadows_iteration=5'] = lambda: ModelCommitment(identifier='alexnet_wo_shadows_iteration=5', activations_model=get_model('al... |
3415910c9da8b9db7c2ffd92eb4e7d8a7180c2f8b108d5e70d00dba46e466cb7 | Python | 405 | 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['Hojin_Jang_ResNet50_Weak_blur'] = lambda: ModelCommitment(identifier='Hojin_Jang_ResNet50_Weak_blur', activations_model=get_model('H... |
798267ee612e9c06bde2ae47b877cb96ceb76e5ef6567d9c48c9e6ee535d9356 | Python | 405 | 19 | # Copyright (C) 2021 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from django.apps import AppConfig
class IAMConfig(AppConfig):
name = "cvat.apps.iam"
def ready(self):
from .signals import register_signals
register_signals(self)
from cvat.a... |
7bdf2c7c8c7e0fba75b344f5705d43df31a8cb996a28a679d92ddb8cbf14af02 | Python | 405 | 7 | from brainscore_vision import benchmark_registry
from .benchmark import Imagenet_C_Noise, Imagenet_C_Blur, Imagenet_C_Weather, Imagenet_C_Digital
benchmark_registry['ImageNet-C-noise-top1'] = Imagenet_C_Noise
benchmark_registry['ImageNet-C-blur-top1'] = Imagenet_C_Blur
benchmark_registry['ImageNet-C-weather-top1'] = I... |
8fad47e7be7311cc5ff88b40e92cc2ceb23047d8ff2b6e614cc8dac33ed1f722 | Python | 405 | 13 | from keras import optimizers
DEFAULT_OPTIMIZER = 'adam'
def get_optimizer(config):
"""Return optimizer specified by configuration."""
config = vars(config)
name = config.get('optimizer', DEFAULT_OPTIMIZER)
optimizer = optimizers.get(name) # Default parameters
lr = config.get('learning_rate')
... |
b8d9bcecf5910ce735bf40f1f7d53314cfb12783c5f98a57491144f7a8d868b8 | Python | 406 | 12 | import pytest
import brainscore_vision
@pytest.mark.travis_slow
def test_artResNet18_1():
model = brainscore_vision.load_model('artResNet18_1')
assert model.identifier == 'artResNet18_1'
# AssertionError: No registrations found for resnet18_random
# ⚡ master ~/vision python -m brainscore_vision score --mode... |
836639126f1aca82b5cce56cfbc16618e481f0a62ec9939739561b5a62f62cc8 | Python | 407 | 17 | from pathlib import Path
from typing import Union
class PathFileIterator:
def __init__(self, path: Union[str, Path]):
self.path = Path(path)
if self.path.is_file():
self._paths = [self.path]
else:
self._paths = list(self.path.glob("*.czi"))
def __iter__(self):
... |
bb2a74c54693b314a083b11354b06660ea294a7ba37091a0cf625f4727ff3ffa | Python | 407 | 12 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from .sam import Sam
from .image_encoder import ImageEncoderViT
from .mask_decoder import MaskDecoder
from .prompt_encoder... |
cd884ab4c2a7cc89b68ab276423dec9574b31a33cb87ecd23887a5d39dba00b4 | Python | 407 | 15 | import nibabel as nib
import numpy as np
import numpy.testing as npt
import AFQ.data.fetch as afd
def test_read_roi():
aff1 = np.eye(4)
template = nib.Nifti1Image(np.ones((10, 10, 10)), aff1)
aff2 = aff1[:]
aff2[0, 0] = -1
roi = nib.Nifti1Image(np.zeros((10, 10, 10)), aff2)
img = afd.read_res... |
1a0e9ff3d82230210d6f30c384956fb4eb1565ed1d07147a7117f579afb1b187 | Python | 408 | 14 | from typing import List
from batchgeneratorsv2.transforms.base.basic_transform import BasicTransform
class ComposeTransforms(BasicTransform):
def __init__(self, transforms: List[BasicTransform]):
super().__init__()
self.transforms = transforms
def apply(self, data_dict, **params):
fo... |
9c22f930f263580a2c325b198c710ee07b5e8a8d956b9f4aeeef5e53c7db5421 | Python | 408 | 22 | import numpy as np
def compute_angle(v1, v2):
"""Compute the angle between two vectors.
Parameters
----------
v1 : array_like
First vector.
v2 : array_like
Second vector.
Returns
-------
angle : float
Angle between the two vectors.
"""
return np.degre... |
0cda7db412913dd626ac918acfcc2c550b9c66d6d2094d8b94261973f3a4d5fb | Python | 409 | 14 | import numpy as np
from ivim.misc import logIo, halfSampleMode
# Test functions
def test_logIo():
for x in [np.random.rand(3), np.random.rand(3,4), np.random.rand(3,4,5)]:
np.testing.assert_allclose(logIo(x), np.log(np.i0(x)),rtol=1e-6,atol=1e-6)
def test_halfSampleMode():
X = np.array([[1,2,3,3,4,0]... |
2e35ef713dcad1956aa4337e6a8c46a50be7cea29fb02763e3352db1ef2680cd | Python | 409 | 36 | # -*- coding: utf-8 -*-
def test_verify_data_vs_sh_order():
# Quite simple, nothing to test
pass
def test_compute_sh_coefficients():
# toDO
pass
def test_compute_rish():
# toDO
pass
def test_peaks_from_sh():
# toDO
pass
def test_maps_from_sh():
# toDO
pass
def test_co... |
7bd468bf97fea2758bcdf18977dc8e60d6f90d1d02f5413a2870740005ecfbe8 | Python | 409 | 12 | # -*- coding: utf-8 -*-
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from .sam import Sam
from .image_encoder import ImageEncoderViT
from .mask_decoder import MaskDeco... |
9bd0136f43b027f1613410046a0da52ae1ef45025ec290e33b3c4a0769824b8b | Python | 409 | 18 | # Generated by Django 3.2.18 on 2023-03-20 17:43
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("webhooks", "0003_alter_webhookdelivery_status_code"),
]
operations = [
migrations.AlterField(
model_name="webhook",
... |
a155e7de1659611ff1b00737e5211e8ccd8ec820298b98de0c81401a87ac8de0 | Python | 409 | 6 |
from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['alexnet_no_specular_iteration=2'] = lambda: ModelCommitment(identifier='alexnet_no_specular_iteration=2', activations_model=get_model('... |
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