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from wonambi.detect.ripple import DetectRipple def test_detect_ripple(): DetectRipple()
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from .sequence import * from .prediction import max_value, weighted_mean from .model import *
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from .scHetG import train_scHetG,louvain, calculate_metric, calculate_ber,preprocess
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from .core import contents, where __all__ = ["contents", "where"] __version__ = "2023.05.07"
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from .emotion_recognition import * from .personal_identification import * from .ssvep import *
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from .layer import BatchNormedConv1d as BatchNormedConv1d from .layer import Conv1d as Conv1d
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from ._dendrogram import interactive_dendrogram from ._clustermap import interactive_clustermap
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from .tsu_benchmark import TSUBenckmarkDataset from .sandiego_ssvep import SanDiegoSSVEPDataset
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from .dgcnn import DGCNN from .lggnet import LGGNet from .gin import GIN from .rgnn import RGNN
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import os import sys sys.path.append(os.path.dirname(os.path.realpath(__file__)) + "/../../src")
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# coding: utf-8 from .Sim3DR import get_normal, rasterize from .lighting import RenderPipeline
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from plantsegtools.proofreading.proofreading import main if __name__ == '__main__': main()
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from . import utils, files, vis, models, types, datasets, metrics __version__ = 'beta_24.07.23'
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""" Recipes for Q-Chem. These recipes will ultimately replace the existing ones in `qchem`. """
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from protonets.models.factory import get_model, register_model import protonets.models.few_shot
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from . import base, interface, manager __all__ = [ "base", "interface", "manager", ]
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from setuptools import find_packages, setup setup( name='src', packages=find_packages() )
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from .extract import Extractor from .classify import Classifier from .aggregate import Aggregator
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from .aalen_johansen import AalenJohansenCompetingRisks __all__ = ["AalenJohansenCompetingRisks"]
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from .data_utils import preprocess, mnn_edges_svd,knn_edges,precluster,precluster_negative_paris
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from setuptools import find_packages, setup setup( name='src', packages=find_packages(), )
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from queso.estimators.flax.dnn import BayesianDNNEstimator __all__ = [ "BayesianDNNEstimator" ]
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from scipy.stats import zscore def transform(data): data = zscore(data, axis=0) return data
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import pstats p = pstats.Stats("profile") p.strip_dirs() p.sort_stats("cumtime") p.print_stats(50)
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from .coreblock import UnlockDNACoreBlock __all__ = ( "UnlockDNACoreBlock", )
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import torch class Tanh(torch.nn.Tanh): def __init__(self) -> None: super().__init__()
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# Copyright (c) OpenMMLab. All rights reserved. from .DPA_TISR import DPATISR __all__ = ['DPATISR']
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__author__ = 'martinez' import vtk import numpy as np from .variables import * from .utils import *
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NUM_TRIALS = 50 STUDY_SAVE_DIR = 'study_saves' K_MIN = 1 K_MAX = 50 ALPHA_MIN = 50 ALPHA_MAX = 5000
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from .scaleddotproductattention import ( ScaledDotProductAttention as ScaledDotProductAttention, )
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# info __version__ = "1.0.0" __author__ = 'Boyi Yu' # init from . import preprocess from . import train
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from setuptools import find_packages, setup setup( name='seq2exp', packages=find_packages(), )
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import math @profile def f(): o = math.sqrt(2013) return o if __name__ == '__main__': f()
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import os import time while 1: os.system('nvidia-smi') time.sleep(1) os.system('cls')
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from plantsegtools.evaluation.evaluation_segmentation import main if __name__ == '__main__': main()
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# Copyright (c) OpenMMLab. All rights reserved. from .sr_backbones import DPATISR __all__ = ['DPATISR']
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from .pipeline import Pipeline from .cryovesnet import * from .cryovesnet import run_default_pipeline
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#! /usr/bin/env python # -*- coding: utf-8 -*- from main import main main("issue561-v4", "issue561-v5")
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def test_gui(): from rastermap.gui import gui def test_class(): from rastermap import Rastermap
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import sys if sys.version_info.major == 3: from pregex.pregex import * else: from pregex import *
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#! /usr/bin/env python # -*- coding: utf-8 -*- from main import main main("issue561-v3", "issue561-v4")
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#! /usr/bin/env python # -*- coding: utf-8 -*- from main import main main("issue561-v2", "issue561-v3")
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from .improc import get_srh_base_aug from .srh_dataset import PatchDataset, SlideDataset, slide_collate_fn
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#!/usr/bin/env python # coding: utf-8 # In[ ]: def say_hello(name) -> str: return f"hello, {name}"
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import os LMC_CONFIG_PATH = os.path.join(os.path.split(os.path.abspath(__file__))[0], 'lmc_config.yaml')
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from .InSTAnT import Instant # from .poisson_binomial import PoissonBinomial # from .poibin import PoiBin
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#! /usr/bin/env python # -*- coding: utf-8 -*- from main import main main("issue561-v1", "issue561-v2")
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import numpy as np import pytest @pytest.fixture(autouse=True) def random_seed(): np.random.seed(42)
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import torch class Sigmoid(torch.nn.Sigmoid): def __init__(self) -> None: super().__init__()
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from .baseline import * from .lambd import * from .compose import * from .lambd import * from .pdb import *
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from ._dists import Distribution from ._envs import Environment __all__ = ["Distribution", "Environment"]
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from .ball_forward import frechet_ball_forward from .hyperboloid_forward import frechet_hyperboloid_forward
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#! /usr/bin/env python # -*- coding: utf-8 -*- from main import main main("issue561-base", "issue561-v1")
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"""Math helpers shared across the codebase.""" from .rbf import wendland_c2 __all__ = ["wendland_c2"]
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from .math_operations import MathOperations as MathOperations from .quantization import quantize as quantize
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import torch class Identity(torch.nn.Identity): def __init__(self) -> None: super().__init__()
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from .batch_normed_conv1d import BatchNormedConv1d as BatchNormedConv1d from .conv1d import Conv1d as Conv1d
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from .tcga_program_dataset import TCGA_Program_Dataset from .tcga_project_dataset import TCGA_Project_Dataset
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from .data import Task, load_data, fmt_grid, load_single from .vis import draw_grid, draw_task, output_drawing
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from .decoder import TransformerDecoder from .embedding import Embeddings from .transformers import swin_base
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from skymap.main import SkyMap from skymap.metrics import SkyMapMetrics __all__ = ["SkyNet", "SkyNetMetrics"]
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"""Initilialize testing of PyPREP modules.""" # Authors: The PyPREP developers # SPDX-License-Identifier: MIT
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import numpy.random as npr from .numpy_wrapper import wrap_namespace wrap_namespace(npr.__dict__, globals())
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from .base_transform import * from .any import * from .label import * from .numpy import * from .torch import *
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DEFAULT_OUTPUT_FORMAT = 'Pharmacology' # Options: 'Pharmacology', 'VM', 'Pharmacology - Megan', 'VM - Megan'
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# Copyright (c) OpenMMLab. All rights reserved. from .sr_backbones import DPATISR_3D __all__ = ['DPATISR_3D']
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from .ball_backward import frechet_ball_backward from .hyperboloid_backward import frechet_hyperboloid_backward
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import torch from .acsconv import ACSConv from .soft_acsconv import SoftACSConv from .conv2_5d import Conv2_5d
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from .operator import InfidelityOperatorUPsi, InfidelityUPsi from . import expect from . import expect_chunked
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"""deepsignal2 uses BiLSTM to detect DNA methylation state from Nanopore reads.""" __version__ = '0.1.3'
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from dataclasses import dataclass @dataclass(eq=True, frozen=True) class Signal: name: str width: int
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from .dynamics_driver import QDynamics from .infidelity_psi import InfidelityOptimizer as InfidelityOptimizerImp
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# SPDX-FileCopyrightText: 2024 Sergiu Deitsch <sergiu.deitsch@gmail.com> # # SPDX-License-Identifier: Apache-2.0
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# Make the class 'ProteinDataset' accessible from 'src.data_handling' from .protein_dataset import ProteinDataset
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from .logger import get_logger, setup_logging, SuppressShuffleWarning from .tensorboard import TensorboardWriter
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from jax.config import config from queso.sensors.tc.sensor import Sensor config.update("jax_enable_x64", True)
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import torch class ReLU(torch.nn.ReLU): def __init__(self) -> None: super().__init__(inplace=False)
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import torch class SiLU(torch.nn.SiLU): def __init__(self) -> None: super().__init__(inplace=False)
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from .callback import Callback from .loader import BatchLoader __all__ = [ 'Callback', 'BatchLoader', ]
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# Written by Minh Nguyen and CBIG under MIT license: # https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
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from .layer import BufferedIdentity as BufferedIdentity from .layer import BufferlessIdentity as BufferlessIdentity
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from .bciciv_2a import BCICIV2aDataset from .strokes import StrokePatientsMIDataset,StrokePatientsMIProcessedDataset
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from elasticai.creator.ir2vhdl import ( InstanceFactory, ) InstanceFactoryForCombinatorial = InstanceFactory()
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import math def calculate_address_width(num_items: int) -> int: return max(1, math.ceil(math.log2(num_items)))
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import pyclesperanto_prototype as cle dict = cle.operations(must_have_categories=['background removal']) print(dict)
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#! /usr/bin/env python # -*- coding: utf-8 -*- from main import main main(revisions=["issue583-v1", "issue583-v2"])
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from .kernel import PW2AOkernel from .aodiag import AODiagKernel from .utils.misc import Timer __version__ = "0.3.0"
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from .build import build_dataset, build_pretraining_dataset __all__ = ['build_dataset', 'build_pretraining_dataset']
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from __future__ import print_function,division import sys def report(*args): print('#', *args, file=sys.stderr)
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from .greedy_search import GreedySearch from .beam_search import BeamSearch __all__ = ["GreedySearch", "BeamSearch"]
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#! /usr/bin/env python # -*- coding: utf-8 -*- from main import main main(revisions=["issue595-v1", "issue595-v2"])
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#! /usr/bin/env python # -*- coding: utf-8 -*- from main import main main(revisions=["issue595-v1", "issue595-v3"])
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"""Modules for processing tree-like morphology reconstructions.""" from treem.morph import * from treem.tree import *
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#! /usr/bin/env python # -*- coding: utf-8 -*- if __name__ == "__main__": from driver.main import main main()
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""" Module to do quantification using SVB surface/volume hybrid mode """ def run(wsp): raise NotImplementedError()
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#! /usr/bin/env python # -*- coding: utf-8 -*- from main import main main(revisions=["issue595-base", "issue595-v1"])
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from setuptools import find_packages, setup setup( name='src', packages=find_packages(), version='0.1.0', )
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from .poly_factory import poly_factory from .matrix_factory import get_matrix __all__ = ["poly_factory", "get_matrix"]
67e4b8a27c74142050e555407c67364b1c37de88c0cf36960d25a8cfc197fd4e
Python
120
6
#! /usr/bin/env python # -*- coding: utf-8 -*- from main import main main(revisions=["issue549-base", "issue549-v1"])
6d50fb6fc410bf27145bc6dc8c263462003c4cf5be76bc16a9c63eedcce55cee
Python
120
6
#! /usr/bin/env python # -*- coding: utf-8 -*- from main import main main(revisions=["issue596-base", "issue596-v1"])