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# encoding : utf-8 """This module implements functionailty for converting to and from neo objects"""
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import dtiplayground.dmri.common.module as module DTIFiberProfileModule = module.DTIPlaygroundModule
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# fmt: off # flake8: noqa from . import config, datasets, metrics, utils from .eval import Evaluator
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import dtiplayground.dmri.common.module as module DTITractographyModule = module.DTIPlaygroundModule
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"""Module defining global constants.""" EPS = 1.0e-10 KNOWN_DATA_SPLITS = ["train", "valid", "test"]
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# from napari_debcr import make_sample_data # add your tests here... def test_something(): pass
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from .data import state_diff, state_loadings, umap __all__ = ["state_diff", "state_loadings", "umap"]
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from . import prep_inputs from importlib.metadata import version __version__ = version("MEGnet-neuro")
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try: from python._version import version as __version__ except ImportError: __version__ = "unknown"
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# Copyright (c) OpenMMLab. All rights reserved. from .misc import add_prefix __all__ = ['add_prefix']
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from . import math from . import stats from . import trials from . import signal from . import analysis
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#!/usr/bin/env python from setuptools import setup setup( setup_requires=['pbr'], pbr=True, )
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import os order="python preprocess_1.py" os.system(order) order="python preprocess_2.py" os.system(order)
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from .extractor import ActivationsExtractor from .executor import BatchExecutor from .inferencer import *
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# -*- coding: utf-8 -*- import importlib.metadata version_string = importlib.metadata.version('scilpy')
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from kinescope.pipeline.pose_pipeline import PoseProcessingPipeline __all__ = ["PoseProcessingPipeline"]
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def main(): """Main entry point of the application""" pass if __name__ == "__main__": main()
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from ._stability import calc_stability from ._entropy import find_best_settings, filter_tuples, calc_entropy
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import logging logger = logging logger.basicConfig(format="%(asctime)s - %(message)s", level=logging.INFO)
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from .n4_bias_corrector import N4BiasCorrector from .sitk.sitk_n4_bias_corrector import SitkN4BiasCorrector
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from .normalizers import RangeNormalizer, UnitGaussianNormalizer from .base_dataprocessor import DataProcessor
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from .validator import PhysicsValidator, ValidationResult __all__ = ['PhysicsValidator', 'ValidationResult']
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# -*- coding: utf-8 -*- def test_convert_freesurfer_into_polydata(): pass def test_flip_LPS(): pass
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#%% from geoseg.models.MixerUnet import ConvMixer_UNET_down model = ConvMixer_UNET_down(num_classes=2) # %%
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#!/usr/bin/env python import sys from ogo.cli.cli import main if __name__ == "__main__": sys.exit(main())
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# -*- coding: utf-8 -*- def test_ttest_two_matrices(): # toDo pass def test_omega_sigma(): pass
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"""Collection of tools for fitting Models with `scipy.optimize.minimize`.""" from .backend import SciPyBackend
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"""TODO: tests for TensorFlowBackend. Need to figure out how to only run these if TensorFlow is installed. """
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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Nov 14 16:30:41 2017 @author: Manish Goel """
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from .util import * from .timer import * from .schedule import * from .shm_cache import * from .logging import *
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# Copyright (C) 2021-2022 Intel Corporation # Copyright (C) CVAT.ai Corporation # # SPDX-License-Identifier: MIT
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from .rbcd_attack import PRBCDAttack, GRBCDAttack __all__ = classes = [ 'PRBCDAttack', 'GRBCDAttack', ]
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from seisbench.ext.utils import get_edge_indices, stack_windows __all__ = ["stack_windows", "get_edge_indices"]
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#!/usr/bin/env python3 # -*- coding: utf-8 -*- __all__ = [ "image_processing", "image_set_processing", ]
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from .egnn_pytorch import EGNN, EGNN_Network from .egnn_pytorch_geometric import EGNN_Sparse, EGNN_Sparse_Network
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import os LOG_PATH = os.path.abspath(os.path.dirname(__file__) + os.sep + '..' + os.sep + '..' + os.sep + 'log')
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from .layers import * NAME_TO_TRANSFORMER_LAYER = { 'rm': RM, 'tm': TM, 'prm': PRM, 'svd': SVD }
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from .model import DLIM from .api import DLIM_API from .dataset import Data_model __version__ = "1.1.0"
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from brainscore_vision import metric_registry from .metric import Accuracy metric_registry['accuracy'] = Accuracy
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import numpy as np def bmi(height, weight): return weight.astype(float)/np.square(height.astype(float)/100)
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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Jun 23 13:40:53 2024 @author: costantino_ai """
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import unittest class TestCorrection(unittest.TestCase): pass if __name__ == '__main__': unittest.main()
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def clamp(x, a, b): return max(a, min(x, b)) def lerp(x, a1, b1, a2, b2): return (x - a1)/(b1 - a1)*(b2 - a2) + a2
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import importlib import unimol_plus.tasks import unimol_plus.data import unimol_plus.models import unimol_plus.losses
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from .t1_centric import preprocess_brats_style_t1_centric from .t1c_centric import preprocess_brats_style_t1c_centric
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# from .poolformer import * from model.InceptionNeXt.inceptionnext import * from model.InceptionNeXt.convnext import *
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import serial ser = serial.Serial() ser.baudrate = 115200 ser.port = "COM3" ser.open() ser.write(b"G1 Z25\r\n")
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from .hd_bet import HDBetExtractor try: from .synthstrip import SynthStripExtractor except ImportError: pass
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"""Contains the entry-point to the ViRelAy application.""" from virelay.application import run_cli_app run_cli_app()
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# Copyright (C) CVAT.ai Corporation # # SPDX-License-Identifier: MIT class WebhookDeliveryError(Exception): pass
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def main(): print("Hello from 2025-zhou-hipct-hierarchical-segmentation!") if __name__ == "__main__": main()
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import json f=open('/data2/whr/czl/TwiBot22-baselines/datasets/Twibot-22/tweet_0.json','r') tweet=json.load(f) print()
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# -*- coding: utf-8 -*- def test_fixel_density(): # toDO pass def test_maps_to_masks(): # toDO pass
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from setuptools import setup, find_packages setup( name='nnobe', version='0.1', packages=find_packages(), )
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from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper, preprocess_images as preprocess_pytorch
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# -*- coding: utf-8 -*- def test_gamma_fit2metrics(): # toDO pass def test_fit_gamma(): # toDO pass
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# from napari_debcr import write_single_image, write_multiple # add your tests here... def test_something(): pass
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"""RG-NFT: Reliability-Gated Neuro-Fuzzy Temporal Fusion for early driver drowsiness prediction.""" __version__ = "0.1.0"
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from brainscore_vision import metric_registry from .metric import ValueDelta metric_registry['value_delta'] = ValueDelta
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from setuptools import setup, find_packages setup( name='humanobe', version='0.1', packages=find_packages(), )
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from brainscore_vision import metric_registry from .ceiling import RSACeiling metric_registry['rsa_ceiling'] = RSACeiling
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from fastapi import FastAPI from container import Container class MainApp: container = Container() app = FastAPI()
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import pathlib def main(): path = pathlib.Path(__file__) print(f'-I{path.parent}') if __name__ == '__main__': main()
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""" Missing data approaches that delete values. """ from .complete_case import complete_case __all__ = ["complete_case"]
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from __future__ import absolute_import from .neuroCombat import neuroCombat from .neuroCombat import neuroCombatFromTraining
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# Module initialization from .gsr import GSR from .gsr_alternative import GSRAlternative __all__ = ['GSR', 'GSRAlternative']
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# content of test_sample.py def func(x): return x + 1 #def test_run(): def test_answer(): assert func(4) == 5
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import pandas as pd df = pd.read_csv(r'/data2/whr/czl/TwiBot22-baselines/datasets/Twibot-22/edge.csv') print(df.to_string())
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import os project = 'run_vp.out' first = 0 last = 999 for i in range(first,last): os.system("./" + project + " " + str(i))
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# -*- coding: utf-8 -*- def test_surface_apply_transfo(): # toDO pass def test_surface_flip(): # toDo pass
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# mps/__init__.py from .supernet import SuperNet, convert_to_supernet, ChoiceConfig from .aihwkit_backend import AnalogConfig
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import sys sys.path.append("..") from utils import get_data_dir DATA_DIR = get_data_dir("206") print(DATA_DIR / "Twibot22")
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#! /usr/bin/env python # aponteeduardo@gmail.com # copyright (C) 2016 '''' ''' if __name__ == '__main__': pass
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"""Version information.""" # The following line *must* be the last in the module, exactly as formatted: __version__ = "4.0.1"
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"""Public GUI entry point for Waven.""" from __future__ import annotations from Waven.zebraGUI import run __all__ = ["run"]
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from .setupDrop import * from . import cli from . import config from . import utils from . import demo __version__ = "1.6.1"
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#! /usr/bin/env python # aponteeduardo@gmail.com # copyright (C) 2017 '''' ''' if __name__ == '__main__': pass
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from model.refer_mixup import * from model.VAEoptimize_model import * from model.DANST_model import * from model.utils import *
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class FeatureVisException(Exception): """ Catch-all exception for errors generated during feature visualization.""" pass
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# -*- coding: utf-8 -*- def test_recognize(): # todo: # bundleseg = BundleSeg(...) # bundleseg.recognize. pass
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from .bootstrap import bootstrap_ci from .logging import setup_logging __all__ = [ "bootstrap_ci", "setup_logging", ]
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from ._ligand_receptor_database import ligand_receptor_database, filter_lr_database from ._spatial_mapping import spatial_mapping
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from .quant import * from .plot import * __author__ = 'David Goertsen' __email__ = 'dgoertsen@caltech.edu' __version__ = '0.0.1'
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from .tests import lrt, wald, wald_repeated, t_test, rank_test, two_sample, pairwise, \ versus_rest, partition, continuous_1d
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# flake8: noqa import deepreg.dataset.loader from deepreg.dataset.preprocess import RandomAffineTransform3D, RandomDDFTransform3D
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from brainscore_vision import benchmark_registry from .benchmark import Objectnet benchmark_registry['ObjectNet-top1'] = Objectnet
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#!/usr/bin/env python3 # -*- coding: utf-8 -*- from .rclone import * from .utils import * from .analysis import BaseAnalysisPlugin
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"""Tutorial scripts, roughly in order of complexity. TODO: convert to jupyter notebooks, move to docs folder with readthedocs. """
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from setuptools import setup, find_packages setup( name='foundcog_analysis', version='0.1', packages=find_packages(), )
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# The hidden import is necessary for pandas 0.22.0+. hiddenimports = ["pandas._libs.tslibs.timedeltas", "pandas._libs.tslibs.base"]
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from .graph_nn import GraphNeuralNetwork from .attention import AttentionLayers __all__ = ["GraphNeuralNetwork", "AttentionLayers"]
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from .cluster_analysis import ClusterAnalysis from .refinement import ClusterTrainer __all__ = ["ClusterAnalysis", "ClusterTrainer"]
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import numpy as np def getFs(opsFpath): ops = np.load(opsFpath, allow_pickle=True).item() fs = ops['fs'] return fs
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from . import plot from . import glove from . import power from . import model from . import multiprocessing as mp from .func import *
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from .version import version as __version__ # noqa from .utils import * # noqa from .api import * # noqa _ga_id = "UA-156363454-3"
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Python
135
8
import numpy as np from typing import Any def logit(x: np.ndarray) -> np.ndarray: ''' ''' return 1 / (1 + np.exp(-x))
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Python
135
3
# Copyright (c) OpenMMLab. All rights reserved. from .evaluation import * # noqa: F401, F403 from .utils import * # noqa: F401, F403
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Python
136
4
from brainscore_vision import benchmark_registry from .benchmark import Imagenet2012 benchmark_registry['ImageNet-top1'] = Imagenet2012
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Python
137
5
from .hotspot import * from .hotspots_3d import * from .interaction_hotspot import * from .coexpression import * from .geometry import *
2491124c0ef0fcaf2feb45db09ddb922a9ab28a1cbcf93a235ec4c1d8195da61
Python
138
7
from .sentence_transformer import SentenceTransformer from .llm import LLM __all__ = classes = [ 'SentenceTransformer', 'LLM', ]