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Python
179
12
import numpy as np def fit_gene_model(gene_counts, umi_counts): N = gene_counts.size mu = np.zeros(N) var = np.ones(N) x2 = np.ones(N) return mu, var, x2
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Python
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# -*- coding: utf-8 -*- # (C) Copyright 2026 IBM. All Rights Reserved. # # Licensed under the MIT license. See LICENSE file in the project root for details. """aihwkit tests."""
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Python
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""" Copyright (c) Meta Platforms, Inc. and affiliates. This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. """
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Python
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""" Copyright (c) Meta Platforms, Inc. and affiliates. This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. """
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Python
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# Replaced with the current commit when building the wheels. commit = "{{RAY_COMMIT_SHA}}" version = "3.0.0.dev0" if __name__ == "__main__": print("%s %s" % (version, commit))
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# encoding: utf-8 """ The *pathspec.patterns* package contains the pattern matching implementations. """ # Load pattern implementations. from .gitwildmatch import GitWildMatchPattern
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Python
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r""" TOSICA (Transformer for One-Stop Interpretable Cell-type Annotation) """ from . import train, pre, TOSICA_model from .train import fit_model from .pre import predict import os
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from .rewards import (RewardCalculator, MORewardCalculator, SumRewardCalculator, BoundedRewardCalculator, MultiplyRewardCalculator, NanodomainsRewardCalculator)
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from ray import serve @serve.deployment class MyDeployment: def __call__(self, model_path): from my_module import my_model self.model = my_model.load(model_path)
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Python
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# -*- encoding: utf-8 -*- from __future__ import division, print_function from . import model, scoring, utils from .core import callers from .model import SegModel __version__ = "0.2.0"
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Python
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from timescoring.annotations import Annotation from timescoring.scoring import EventScoring, Scoring, SampleScoring __all__ = ["Annotation", "Scoring", "SampleScoring", "EventScoring"]
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import random import numpy as np import torch def setup_random_seed(seed): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed)
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""" AVA package for organizing different kinds of data Contains -------- `ava.data.data_container` DataContainer class for linking directories containing different sorts of data. """
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Python
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from .vae_model import VAE from .annot_vae_model import AnnotVAE from .atac_reg_model import ATACRegModel from .refine_model import RefineODE from .velocity_field import VelocityFieldReg
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# -*- coding: utf-8 -*- # (C) Copyright 2026 IBM. All Rights Reserved. # # Licensed under the MIT license. See LICENSE file in the project root for details. """Neural network modules."""
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# -*- coding: utf-8 -*- """ Freeze pytest.main() with vtkplotlib included. """ import sys import vtkplotlib import pytest sys.exit(pytest.main(sys.argv[1:] + ["--no-cov", "--tb=native"]))
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import torch from ..base import Transform class Reverse(Transform): """Reverse the signal in time.""" def apply(self, x, generator=None): return torch.flip(x, dims=[-1])
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Python
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# -*- coding: utf-8 -*- # (C) Copyright 2026 IBM. All Rights Reserved. # # Licensed under the MIT license. See LICENSE file in the project root for details. """Experiments for aihwkit."""
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Python
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# Copyright (C) 2018-2022 Intel Corporation # # SPDX-License-Identifier: MIT from cvat.utils.version import get_version VERSION = (2, 80, 1, "alpha", 0) __version__ = get_version(VERSION)
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"""Vendored graph neural network helpers used by gsMap latent learning.""" from .model import gat_model from .train import model_trainer __all__ = [ "gat_model", "model_trainer", ]
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""" Reporting code for tedana """ from .html_report import generate_report from .static_figures import comp_figures, pca_results __all__ = ["generate_report", "comp_figures", "pca_results"]
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# -*- coding: utf-8 -*- # (C) Copyright 2026 IBM. All Rights Reserved. # # Licensed under the MIT license. See LICENSE file in the project root for details. """Helpers for aihwkit tests."""
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import GR_pKa.data import GR_pKa.features import GR_pKa.models import GR_pKa.train import GR_pKa.args import GR_pKa.constants import GR_pKa.interpret import GR_pKa.nn_utils import GR_pKa.utils
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# -*- coding: utf-8 -*- # (C) Copyright 2026 IBM. All Rights Reserved. # # Licensed under the MIT license. See LICENSE file in the project root for details. """Analog RNN related modules."""
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r""" Core functions of SLAT model """ from .utils import * from .loaddata import load_anndata, load_anndatas from .preprocess import Cal_Spatial_Net, scanpy_workflow from .prematch import icp
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class Faker: """Mock Faker class to test fake_email_creator.py. Meant to mock https://github.com/joke2k/faker package. """ def email(self) -> str: return "fake@fake.com"
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from epilepsy2bids.annotations import Annotation, Annotations, EventType, SeizureType from epilepsy2bids.eeg import Eeg __all__ = ["Annotation", "Annotations", "SeizureType", "EventType", "Eeg"]
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""" Copyright (c) Meta Platforms, Inc. and affiliates. This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. """ # Utility scripts
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from django import template register = template.Library() @register.simple_tag def get_verbose_field_name(instance, field_name): return instance._meta.get_field(field_name).verbose_name.title()
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_base_ = 'faster-rcnn_r50_fpn_2x_ruod.py' model = dict( backbone=dict( depth=101, init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101')))
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from .breakpoint import Breakpoint from .pesr_test import PESRTest from .sr_test import SRTest, SRTestRunner from .pe_test import PETest, PETestRunner from .pesr_test import PESRTest, PESRTestRunner
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from .operator import Operator from .site_operator import ( sigma_x, sigma_y, sigma_z, sigma_p, sigma_m, ) from .common_operators import ( Heisenberg, Ising, )
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# -*- coding: utf-8 -*- # (C) Copyright 2026 IBM. All Rights Reserved. # # Licensed under the MIT license. See LICENSE file in the project root for details. """Utilities and helpers for aihwkit."""
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Python
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# Ultralytics YOLO 🚀, GPL-3.0 license from .track import register_tracker from .trackers import BOTSORT, BYTETracker __all__ = 'register_tracker', 'BOTSORT', 'BYTETracker' # allow simpler import
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from importlib.metadata import version as _version __all__ = ["__version__"] __version__ = _version("flowsig") from . import preprocessing as pp from . import tools as tl from . import plotting as pl
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_base_ = 'faster-rcnn_r50_fpn_2x_urpc-coco.py' model = dict( backbone=dict( depth=101, init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101')))
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"""Compatibility import for historical validation scripts.""" import sys from pathlib import Path sys.path.insert(0,str(Path(__file__).resolve().parents[1])) from mothra_runtime import DeduplicatingAdam
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_base_ = 'faster-rcnn_r50_fpn_1x_urpc-coco.py' model = dict( backbone=dict( depth=101, init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101')))
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from gym_tasks.envs.VisualOlfactoryAttentionSwitchEnv import VisualOlfactoryAttentionSwitchEnv from gym_tasks.envs.VisualOlfactoryAttentionSwitchNoBlankEnv import VisualOlfactoryAttentionSwitchNoBlankEnv
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"""Jarvis persistent memory — SQLite-backed, FTS5-searchable knowledge store.""" from .store import MemoryStore, MemoryDocument, MemoryFolder __all__ = ["MemoryStore", "MemoryDocument", "MemoryFolder"]
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import sys from pathlib import Path # make hippunfold/workflow/lib importable as `lib`, as it is for workflow scripts sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "hippunfold" / "workflow"))
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from .directories import * from .network import * from .network_view import * from .dynamics import * from .initialization import * from .ensemble_view import * from .ensemble import * from .stimulus import *
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# Add lecun_normal weight initialization for networks in pytorch from pfrl.initializers.chainer_default import init_chainer_default # NOQA from pfrl.initializers.lecun_normal import init_lecun_normal # NOQA
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from bazel.gen_extract import gen_extract if __name__ == "__main__": gen_extract( [ "cpp/ray_cpp_pkg.zip", ], clear_dir_first=[ "ray/cpp", ], )
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"""Animations.""" from .activations import * from .animations import * from .hexflow import * from .hexscatter import * from .imshow import * from .traces import * from .sintel import * from .network import *
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import setuptools if __name__ == "__main__": with open('requirements.txt') as f: required = f.read().splitlines() setuptools.setup(name="ImputeHiFI",version="0.1.3",install_requires=required,)
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from setuptools import find_packages, setup setup( name='src', packages=find_packages(), version='0.1.0', description='fMRI BOLD auto-regression', author='FrancoisPgm', license='MIT', )
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# 导入 YOLOv8 from ultralytics import YOLO # 载入预训练权重 model = YOLO("last.pt") # 指定 opset=11 并且使用 onnx-sim 简化 ONNXsuccess = model.export(format="onnx", opset=11, simplify=True)
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from bazel.gen_extract import gen_extract if __name__ == "__main__": gen_extract( [ "java/ray_java_pkg.zip", ], clear_dir_first=[ "ray/jars", ], )
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brats_preprocessed_folder = "/media/fabian/DeepLearningData/BraTS2017_preprocessed" brats_folder_with_downloaded_train_data = "/media/fabian/DeepLearningData/Brats17TrainingData" num_threads_for_brats_example = 8
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# -*- coding: utf-8 -*- # (C) Copyright 2026 IBM. All Rights Reserved. # # Licensed under the MIT license. See LICENSE file in the project root for details. """Helpers for the AIHW Composer API (version 1)."""
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Python
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from setuptools import find_packages, setup setup( name='language', packages=find_packages(), version='0.1.0', description='language cort project', author='Maedbh King', license='MIT', )
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# This work is licensed under Attribution-NonCommercial 4.0 International License. # noqa from .zero_dce import ZeroDCE from .zero_dce_generator import ZeroDCEGenerator __all__ = ['ZeroDCEGenerator', 'ZeroDCE']
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from .bioprocessor import BioProcessor from .diseaseprocessor import DiseaseProcessor from .chemicalprocessor import ChemicalProcessor from .geneprocessor import GeneProcessor from .class_entities import Entities
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# -*- coding: utf-8 -*- # (C) Copyright 2026 IBM. All Rights Reserved. # # Licensed under the MIT license. See LICENSE file in the project root for details. """Protobuf definitions for the AIHW Composer API."""
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# Default max_concurrency option in @ray.remote for async actors. DEFAULT_MAX_CONCURRENCY_ASYNC = 1000 LOGGING_ROTATE_BYTES = 512 * 1024 * 1024 # 512MB. LOGGING_ROTATE_BACKUP_COUNT = 5 # 5 Backup files at max.
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import matplotlib from nilearn import plotting matplotlib.use("Agg") # 3D surface # requires `snakemake.input.surf` fig = plotting.plot_surf(snakemake.input.surf, view="dorsal") fig.savefig(snakemake.output.png)
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import sys import pytest if __name__ == "__main__": exit_code = pytest.main(sys.argv[1:]) if exit_code is pytest.ExitCode.NO_TESTS_COLLECTED: exit_code = pytest.ExitCode.OK sys.exit(exit_code)
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""" Copyright (c) Meta Platforms, Inc. and affiliates. This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. """ from __future__ import annotations
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# -*- coding: utf-8 -*- # (C) Copyright 2026 IBM. All Rights Reserved. # # Licensed under the MIT license. See LICENSE file in the project root for details. """Helpers for version 1 of the AIHW Composer format."""
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Python
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# -*- coding: utf-8 -*- # (C) Copyright 2026 IBM. All Rights Reserved. # # Licensed under the MIT license. See LICENSE file in the project root for details. """Client for connecting to the the AIHW Composer API."""
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from .core import * __all__ = ['scTenifoldNet', 'scTenifoldKnk', "sc_QC", "make_networks", "manifold_alignment", "d_regulation", "compare_networks", "virtual_knockout"] __version__ = "0.2.0"
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from ._SimpleTwoArmBandit import SimpleTwoArmBandit from ._ContextualBandit import ContextualBandit from ._Polynomial import Polynomial from ._MixedCSW import MixedCSW from ._CSW import CSW # from ._Waves import Waves
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# from .losses import from .utils import criterions, datasets from .print_model import print_model, print_flops from .data_tools import DataAugment, FeatAugment, MedianAge from .config_handler import YamlHandler
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from .cmbFormer import CmbFormer_S, CmbFormer_B from .resnet3D import resNet3D_18, resNet3D_50, resNet3D_101 from .uniformer import uniformer_xxs, uniformer_xs from .uniformerv2 import UniFormerv2_b16, UniFormerv2_114
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from . import Bayes_fun from . import Extract_raw_data from . import GUI from . import Metrics_fun from . import Overlord from . import Param_fun from . import Save_utils from . import utils from . import AssignUniqueID
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# List of files to exclude from the Ray directory when using runtime_env for # Ray development. These are not necessary in the Ray workers. RAY_WORKER_DEV_EXCLUDES = ["raylet", "gcs_server", "cpp/", "tests/", "core/src"]
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from .auxiliary import vcorrcoef from .graph import gene_graph from .correlation import correlation from .modules import scdemon # Make the modules class __version__ = "0.0.3" __author__ = "Carles Boix, Benjamin James"
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import rlexp from repetition_config import config # set random seeds rlexp.utils.seed(config) # run experiment agent, log = rlexp.exp.continual.run_experiment(config=config) log.config.numpy_log_mode = 'npz' log.save()
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__author__ = "Benoit Turcotte, Albert Michaud-Gagnon, Anthony Bilodeau, and Audrey Durand" __major__ = "0" __minor__ = "0" __revision__ = "2" VERSION = ".".join((__major__, __minor__, __revision__)) __version__ = VERSION
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from ocpmodels.tasks.task import BaseTask from ocpmodels.common.registry import registry @registry.register_task("compute_stats") class ComputeStatsTask(BaseTask): def run(self): self.trainer.compute_stats()
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from ._plot_tensor import plot_tensor,plot_tensor_single,plot_tensor_pathway,plot_pathway from ._plot_utils import plot_genes_list,plot_top_genes,plot_para_hist,plot_sankey,plot_landscape,infer_lineage,plot_tensor_heatmap
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from abc import abstractmethod, ABC class BaseForwardModel(ABC): @abstractmethod def update_params(self, params): pass @abstractmethod def compute_forward_model(self, q): pass
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from samri.manipulations import flip_axis def test_flip_axis(): nii_path = '/usr/share/samri_bidsdata/bids_collapsed/sub-4007/ses-ofM/func/sub-4007_ses-ofM_task-JogB_acq-EPIlowcov_run-0_bold.nii.gz' flip_axis(nii_path)
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__author__ = "Benoit Turcotte, Albert Michaud-Gagnon, Anthony Bilodeau, and Audrey Durand" __major__ = "0" __minor__ = "0" __revision__ = "4" VERSION = ".".join((__major__, __minor__, __revision__)) __version__ = VERSION
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# Trigger pytest hook to automatically zip test cluster logs to archive dir on failure from ray.tests.conftest import pytest_runtest_makereport # noqa from ray.tests.conftest import _isolate_token_auth_state # noqa: F401
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# query_cache.py from collections import OrderedDict from ttl_cache import TTLCache import os class QueryCache(TTLCache): def __init__(self, maxsize=1000, ttl=3600): super().__init__(maxsize=maxsize, ttl=ttl)
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import numpy as np from siapy.entities import SpectralImage # Create a SpectralImage from a numpy array - mostly for testing array = np.zeros((100, 100, 10)) # height, width, bands image = SpectralImage.from_numpy(array)
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# -*- coding: utf-8 -*- from .figure import Figure from .figure_manager import ( gcf, scf, auto_figure, show, save_fig, screenshot_fig, close, reset_camera, view, zoom_to_contents, )
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def test_crop2d(): import torch from sinabs.layers import Cropping2dLayer sinabs_layer = Cropping2dLayer(((2, 3), (3, 1))) inp = torch.zeros((2, 2, 10, 10)) assert sinabs_layer(inp).shape == (2, 2, 5, 6)
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from ...utils import is_torch_available, is_transformers_available if is_transformers_available() and is_torch_available(): from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
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""" Copyright (c) Meta Platforms, Inc. and affiliates. This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. Tests for the OMat24 data processing modules. """
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""" Copyright (c) Meta Platforms, Inc. and affiliates. This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. """ # Workflow orchestration and processing scripts
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# -*- coding: utf-8 -*- # (C) Copyright 2026 IBM. All Rights Reserved. # # Licensed under the MIT license. See LICENSE file in the project root for details. """Linear Algebra functionality.""" from .matrix import AnalogMatrix
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from typing import Any from multimethod import multimethod from data_profiling.config import Settings @multimethod def get_table_stats(config: Settings, df: Any, variable_stats: dict) -> dict: raise NotImplementedError()
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import pkgutil __all__ = [] for loader, module_name, is_pkg in pkgutil.walk_packages(__path__): __all__.append(module_name) module = loader.find_module(module_name).load_module(module_name) exec('%s = module' % module_name)
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from rising.random.abstract import AbstractParameter from rising.random.continuous import ContinuousParameter, NormalParameter, UniformParameter from rising.random.discrete import DiscreteCombinationsParameter, DiscreteParameter
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from .main import CAST_STACK, CAST_MARK, CAST_PROJECT from .CAST_Stack import reg_params, region_detect, corr_dist, CAST_STACK_rough from .visualize import kmeans_plot_multiple, plot_mid, dsplot, plot_mid_v2 from .utils import *
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def ask_yes_no(question): while True: choice = input("{} [y/N]: ".format(question)).lower() if choice in ["y", "ye", "yes"]: return True elif choice in ["n", "no"]: return False
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# -*- coding: utf-8 -*- # (C) Copyright 2026 IBM. All Rights Reserved. # # Licensed under the MIT license. See LICENSE file in the project root for details. """Conversion utilities for interacting with the AIHW Composer API."""
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# -*- coding: utf-8 -*- # (C) Copyright 2026 IBM. All Rights Reserved. # # Licensed under the MIT license. See LICENSE file in the project root for details. """Functionality related to the cloud client for AIHW Composer API."""
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from .ualign_training import calc_trans_loss, preeval, pretrain from .ualign_training_ddp import ddp_preeval, ddp_pretrain __all__ = [ 'calc_trans_loss', 'preeval', 'pretrain', 'ddp_preeval', 'ddp_pretrain', ]
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Python
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from .utils import * from .bgzipfile import BgzipFile from .helpers import reciprocal_overlap, overlap_frac from .multi_tabixfile import MultiTabixFile from .genotype_merging import update_best_genotypes from .rdtest import RdTest
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from .graph import ( EventData, NIRGraphData, NIRNodeData, TimeGriddedData, ValuedEventData, ) __all__ = [ "EventData", "NIRGraphData", "NIRNodeData", "TimeGriddedData", "ValuedEventData", ]
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from bazel.gen_extract import gen_extract if __name__ == "__main__": gen_extract( [ "ray_redis.zip", ], clear_dir_first=[ "ray/core/src/ray/thirdparty/redis/src", ], )
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Python
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""" Copyright (c) Meta Platforms, Inc. and affiliates. This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. """ from __future__ import annotations VERSION = "1.0.0"
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"""The model module handles all logic/calculations, e.g. calculate statistics, testing for special conditions.""" from data_profiling.model.description import BaseAnalysis, BaseDescription __all__ = ["BaseAnalysis", "BaseDescription"]
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"""This module defines the base class for object scanning and gets rid of reference cycles.""" from ray.util.annotations import DeveloperAPI @DeveloperAPI class DAGNodeBase: """Common base class for a node in a Ray task graph."""
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# -*- coding: utf-8 -*- # (C) Copyright 2026 IBM. All Rights Reserved. # # Licensed under the MIT license. See LICENSE file in the project root for details. """Analog hardware library for PyTorch.""" from .version import __version__
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#!/usr/bin/env python3 # Copyright 2004-present Facebook. All Rights Reserved. from deep_sdf.data import * from deep_sdf.mesh import * from deep_sdf.metrics.chamfer import * from deep_sdf.utils import * from deep_sdf.workspace import *