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Python
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from .pair_potential import ZBLE __all__ = [ ZBLE, ]
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Python
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"""Calculation-focused validation helpers and runners."""
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Python
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from . import test_bruteforce from . import test_validate
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from .base import WaveFunction __all__ = ["WaveFunction"]
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from __future__ import annotations __version__ = "1.2.3"
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from .cell_emb import get_batch_cell_embeddings, embed_data
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from . import help_functions __all__ = ["help_functions"]
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from __future__ import annotations __version__ = "0.1.0"
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"""Documentation build and notebook-maintenance helpers."""
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from tasks.runner import run_task __all__ = ["run_task"]
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"""kanfood: leakage-free FTIR food-adulteration pipeline."""
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from .main import main if __name__ == "__main__": main()
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"""Visualization of aspects of the compositionality study."""
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Python
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from .bruteforce import brute_force __all__ = ["brute_force"]
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Python
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"""Exploratory data analysis for the compositionality study."""
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from .orbformer_se import OrbformerSE __all__ = ["OrbformerSE"]
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"""Utility functions for NIRS cluster-based power simulations."""
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from .config import Config from .rejection import RejectionPolicy
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Python
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import pytest def validate_and_get_frame_dur(): assert False
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from s2_GOF_AD import main if __name__ == "__main__": main()
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from .classification import Accuracy __all__ = [ "Accuracy", ]
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from .mobile_unet_v3 import MobileUNetV3 __all__ = ["MobileUNetV3"]
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"""Data loading and preprocessing for the compositionality study."""
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from .criterions import * from .models import * from .tasks import *
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from __future__ import print_function from __future__ import division
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from merge_GOF_AD import main if __name__ == "__main__": main()
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from .FunctionalCPD import FunctionalCPD __all__ = ["FunctionalCPD"]
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from . import frame_classification __all__ = ["frame_classification"]
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from .modeling import * from ._deeplab import convert_to_separable_conv
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from .engine import * from .utils import log_print from .model import *
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from .dimenet import DimeNetppEncoder from .gemnet import GemNetTEncoder
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from .utils import is_valid_cmd, get_mount_docker, get_directory # noqa
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# models/__init__.py from .ConvMLP import ConvMLP from .CNN import CNN
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from __future__ import annotations def main() -> None: return None
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from .fasta_tools import fasta_extraction __all__ = ["fasta_extraction"]
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from .allegro_models import AllegroModelE __all__ = [ AllegroModelE, ]
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from __future__ import annotations class PoetryError(Exception): pass
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from .split_subset import split_subset from .merge_subset import merge_subset
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import pathlib from typing import Union PathLike = Union[str, pathlib.Path]
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from s2_GOF_pTau_ratio import main if __name__ == "__main__": main(0.7)
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from s2_GOF_pTau_ratio import main if __name__ == "__main__": main(0.3)
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from s2_GOF_pTau_ratio import main if __name__ == "__main__": main(0.4)
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from s2_GOF_pTau_ratio import main if __name__ == "__main__": main(0.6)
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"""Core implementation for deep learning-enhanced Raman microspectroscopy."""
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"""Example module""" from __future__ import annotations __version__ = "0.1"
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from .scatter import scatterc, scatterm, scattern from .colors import Colorizer
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"""UNet segmentation model.""" from .unet import UNet __all__ = ["UNet"]
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from .cnn import CNNBinaryClassifierV3, CNNBinaryClassifier2V1, BINND, BINNDLite
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# encoding: utf-8 """ @author: Jiayang Chen @contact: yjcmydkzgj@gmail.com """
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# this file is needed here to include configs when building project as a package
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from ._model import GIMVI from ._module import JVAE __all__ = ["GIMVI", "JVAE"]
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# encoding: utf-8 """ @author: sherlock @contact: sherlockliao01@gmail.com """
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# NPZ datasets: ``WMB``, ``CA1``; ``DLBaseDataModule``; ``datasets.preprocess``.
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from .multires_hubert import * # noqa from .multires_hubert_asr import * # noqa
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ori = "_original" ali = "_aligned" nii = ".nii.gz" hdf = ".hdf5" dtype = "float32"
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from .datasets import load_openneuro_bids from .simulated import load_simulated_raw
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from . import __meta__ from .core import Experiment __version__ = __meta__.version
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from ._model import SysVI from ._module import SysVAE __all__ = ["SysVI", "SysVAE"]
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"""Code for the preparation and analysis of the planned functional MRI experiment."""
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from enigmatoolbox._version import __version__ # Default rendering OFF_SCREEN = False
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from ._model import SCAR from ._module import SCAR_VAE __all__ = ["SCAR", "SCAR_VAE"]
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from .cross_disorder import cross_disorder_effect __all__ = ['cross_disorder_effect']
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from .draw_confusion_matrix import plot_confusion_matrix from .draw_roc import plot_roc
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def silentRm(filename): try: os.unlink(filename) except: pass
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"""Training-time utilities: Lightning stack, callbacks, facets, seed, LR schedulers."""
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from .LinearGaussianCPD import LinearGaussianCPD __all__ = [ "LinearGaussianCPD", ]
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from ._model import VELOVI from ._module import VELOVAE __all__ = ["VELOVI", "VELOVAE"]
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from .grn import GeneEmbedding from .cell_emb import get_batch_cell_embeddings, embed_data
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from .embed import has_sklearn if has_sklearn: from .embed import DiffusionMapEmbedding
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# -*- coding: utf-8 -*- """Reusable analysis helpers for the working-memory EEG project."""
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from setuptools import setup setup( version='1.0', packages=['src'], #same as name )
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved __version__ = "0.1"
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from . import resnet from . import mobilenetv2 from . import hrnetv2 from . import xception
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import sys for idx, line in enumerate(sys.stdin): print(f"utt{idx:010d} {line}", end='')
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from . import defaults, frame_labels # noqa : F401 from .transforms import * # noqa : F403
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"""Legacy StructureScore coverage does not contain discrete LogLikeliHood-specific tests."""
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from ._model import RESOLVI from ._module import RESOLVAE __all__ = ["RESOLVAE", "RESOLVI"]
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from __future__ import annotations __import__("pkg_resources").declare_namespace(__name__)
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from fitter import Fitter import scipy.stats as stats import numpy as np import pandas as pd
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from .ranger import Ranger from .ranger913A import RangerVA from .rangerqh import RangerQH
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from __future__ import annotations from poetry.vcs.git.backend import Git __all__ = ["Git"]
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# Copyright (c) Microsoft Corporation. # Licensed under the MIT License. __version__ = "0.1.0"
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# Copyright (c) Microsoft Corporation. # Licensed under the MIT License. __version__ = "1.0.0"
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""" Genetically informed spatial mapping of cells for complex traits """ __version__ = "1.73.8"
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"""Enable ``python -m crossda``.""" from .cli import main if __name__ == "__main__": main()
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import os os.environ["SEGREF3D_DISABLE_SAM2"] = "1" os.environ["SEGREF3D_EDITION"] = "local-cpu"
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from fitter import Fitter import scipy.stats as stats import numpy as np import pandas as pd from.
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from __future__ import annotations import logging POETRY_FILTER = logging.Filter(name="poetry")
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from fitter import Fitter import scipy.stats as stats import numpy as np import pandas as pd from
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from setuptools import find_packages, setup setup( name='src', packages=find_packages(), )
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from __future__ import annotations from poetry.puzzle.solver import Solver __all__ = ["Solver"]
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from .multi_system_sampler import StreamMultiSystemSampler __all__ = ["StreamMultiSystemSampler"]
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from setuptools import find_packages, setup setup( name='code', packages=find_packages(), )
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from .molecule import Molecule from .train import train __all__ = [ "Molecule", "train", ]
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from fitter import Fitter import scipy.stats as stats import numpy as np import pandas as pd from .
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from performer_pytorch.performer_pytorch import PerformerLM, Performer, FastAttention, SelfAttention
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from ._model import TOTALANVI from ._module import TOTALANVAE __all__ = ["TOTALANVI", "TOTALANVAE"]
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# global parameters for drawing pulse colours c_list = ['w', 'r', 'b', 'm', 0.75, 'g', 'c', 'y', 0.5]
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from ._model import Tangram from ._module import TangramMapper __all__ = ["Tangram", "TangramMapper"]
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from .distance import CharacterErrorRate, Levenshtein __all__ = ["Levenshtein", "CharacterErrorRate"]