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from .barreto_2018_sfql import Barreto2018SFQL from .nec_agent import NECAgent from .sf_nec_agent import SFNECAgent
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import os SRC_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) ROOT_DIR = os.path.dirname(SRC_DIR)
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from .fit_model import fit_model from .prepare_data import prepare_data from .refine_tomogram import refine_tomogram
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from .permutation import ( DataColumns, MutationPermutationPipeline, ThermodynamicPermutationPipeline, )
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_base_ = [ './uod-air_retinanet_r50_ufpn_1x_urpc-coco.py', ] train_dataloader = dict(batch_size=4, num_workers=4)
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from setuptools import setup, find_packages setup( name='lop', packages=find_packages(), version='1.0.0' )
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__version__ = "0.3.3" __author__ = "Manu Setty, Zi-Ning Choo, Sitara Persad" __author_email__ = "scp2152@columbia.edu"
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from pathlib import Path import pandas as pd app_dir = Path(__file__).parent tips = pd.read_csv(app_dir / "tips.csv")
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from .preprocess import preprocess_image from .b_net import b_net, b_k_net, b_k_net, b_d_net from .bl_net import bl_net
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from .blocks import * from .model import * from .pipeline import * from .dataloader import * from .post_process import *
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# example_module.py # fmt: off # __is_even_begin__ def is_even(x): return (x % 2) == 0 # __is_even_end__ # fmt: on
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from .data_samples import DataSample, OptSampleList, SampleList __all__ = ['DataSample', 'SampleList', 'OptSampleList']
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__version__ = "2.4.0" def print_version(): print(f"v{__version__}") if __name__ == "__main__": print_version()
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import rlexp.agent import rlexp.env import rlexp.exp import rlexp.utils import rlexp.approximation __version__ = '0.0.0'
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from ray._private.runtime_env.image_uri import ImageURIPlugin def get_image_uri_plugin_cls(): return ImageURIPlugin
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from .base import add_integration_callbacks, default_callbacks __all__ = 'add_integration_callbacks', 'default_callbacks'
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from .tienet import * # noqa: F401,F403 from .unet import * # noqa: F401,F403 from .zero_dce import * # noqa: F401,F403
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import json with open(snakemake.output.json, "w") as f: json.dump(snakemake.params.template_description, f, indent=4)
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VERSION = "1.0.1" vVERSION = "v" + VERSION URL_ARCHIVE = "https://github.com/Matteo-Valerio/pyBioPortal/raw/master/archive"
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#!/usr/bin/env python # -*- coding:utf-8 -*- # @FileName :__init__.py # @Time :2024/3/29 10:38 # @Author :Luni Hu
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from .batch_correction import * from .trad_velocity import * from .check_velocity import * from .cell_trajectories import *
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from setuptools import setup, find_packages setup( name='core', version='1.0.0', packages=find_packages(), )
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from chromoformer.net import ChromoformerClassifier, ChromoformerRegressor from chromoformer.data import ChromoformerDataset
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from bazel.gen_extract import gen_extract if __name__ == "__main__": gen_extract(["java/pom_files.zip"], sub_dir="java")
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from setuptools import setup setup(name='project_fxns', version='0.1', author='Jared Trok', packages=['project_fxns'])
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from fhe import * pailler = Paillier("paillier", 10, 2048, 17, 13, "resources/random_params/", "../resources/cryptoparams/");
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import ray ray.init() @ray.remote(num_cpus=1) def f(): pass print("Hanging...") ray.get(f.remote()) print("Success!")
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from bazel.gen_extract import gen_extract if __name__ == "__main__": gen_extract(["java/maven_deps.zip"], sub_dir="java")
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from .config import PalmConfig from .model import PalmPCAGabor __all__ = ["PalmConfig", "PalmPCAGabor"] __version__ = "1.0.0"
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from setuptools import Extension, setup setup(ext_modules=[Extension(name="vamb._vambtools", sources=["src/_vambtools.pyx"])])
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# -*- coding: utf-8 -*- """ Created on Thu Feb 2 16:10:29 2023 @author: Daniel Granados """ print('Python for Neuron')
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MIME_TYPES = { "text/plain": [".err", ".out", ".log"], } LOG_GRPC_ERROR = "log_grpc_status" # 10 seconds GRPC_TIMEOUT = 10
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"""Utilities for writing structured reports.""" from .report import ReportWriter, Section __all__ = ["ReportWriter", "Section"]
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# from .dynapcnn import DynapcnnConfigBuilder from .speck2e import Speck2EConfigBuilder from .speck2f import Speck2FConfigBuilder
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from .base_edit_model import BaseEditModel from .base_generator import BaseGenerator __all__ = ['BaseEditModel', 'BaseGenerator']
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from enum import Enum class AdversarialLosses(Enum): VANILLA = "vanilla" HINGE = "hinge" LEAST_SQUARE = "least_square"
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from __future__ import absolute_import from .transform import * from .generate import * from .rawdata import * from .prepare import *
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from .twod_object_collection import TwoDObjectCollection from .barreto_2018_twod_object_collection import Barreto2018TwoDObjectCollection
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from .tienet import TIENetEnhanceModel from .tienet_generator import TIENetGenerator __all__ = ['TIENetEnhanceModel', 'TIENetGenerator']
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from ._plotting import * __all__ = ["plot_network_graph", "plot_network_heatmap", "plot_hist", "plot_embedding", "plot_qqplot"]
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from .unimol_pka import UniMolPKAModel from .transformer_encoder_with_pair import TransformerEncoderWithPair from .unimol import UniMolModel
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SOMA = 1 AXON = 2 BASAL_DENDRITE = 3 APICAL_DENDRITE = 4 CUT_DENDRITE = 10 NO_RECONSTRUCTION = 20 TYPE_30 = 30 SPACING = [.1144, .1144, .28]
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from pfrl.envs.multiprocess_vector_env import MultiprocessVectorEnv # NOQA from pfrl.envs.serial_vector_env import SerialVectorEnv # NOQA
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from vxm_model.torch import layers from vxm_model.torch import networks from vxm_model.torch import losses from vxm_model.torch import utils
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from django.apps import AppConfig class InferConfig(AppConfig): default_auto_field = "django.db.models.BigAutoField" name = "infer"
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from fire import Fire from ._xiyue_wang import extract_xiyuewang_features_ if __name__ == "__main__": Fire(extract_xiyuewang_features_)
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from __future__ import print_function from __future__ import division from .layers import * from .models import * from .utils import *
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# Ultralytics YOLO 🚀, GPL-3.0 license from ultralytics.yolo.v8 import classify, detect, segment __all__ = 'classify', 'segment', 'detect'
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from importlib import metadata try: __version__ = metadata.version("siapy") except metadata.PackageNotFoundError: __version__ = "0.0.0"
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from ci.ray_ci.doc.mock.mock_module import MockClass, mock_function # classes and functions __all__ = [ "MockClass", "mock_function", ]
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from .utils import * from .calculate_PI import * from .PROST import * from .plot import * __author__ = " Yuchen Liang " __version__ = " 1.1.2 "
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from . import models from .wrappers import WrapPyTorch, GymnasiumWrapper from .hooks import EvaluationActionHook from .agents import HumanAgent
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import torch from typing import Literal import cebra.registry cebra.registry.add_helper_functions(__name__) from cebra.dynamics.linear import *
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from data_profiling.report.presentation.core import HTML class HTMLHTML(HTML): def render(self) -> str: return self.content["html"]
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from abc import abstractmethod, ABC class BaseObservationOperator(ABC): @abstractmethod def get_observations(self, state): pass
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from . import core from . import utils_via from . import windmap from . import plotting_via from . import plotting_via_ov from . import datasets_via
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from ..base import Transform class Invert(Transform): """Flip the signal polarity.""" def apply(self, x, generator=None): return -x
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from ._dynamical_analysis import dynamical_iteration from ._construct_landscape import construct_landscape from ._pathway_analysis import compute_pathway
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from .cpx_link import link_cpx from .cpx_link import link_cpx_V2 from .complex_sv import ComplexSV from .rescan_single_enders import rescan_single_ender
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def convert_time_pyNN_to_nest(sim, offset): if sim.simulator.state.t > 0: return offset + sim.get_time_step() else: return offset
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from __future__ import print_function, unicode_literals, absolute_import, division from .plot_utils import * from .utils import * from .utils import _raise
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def get_logging_configurator(): from ray._private.ray_logging.logging_config import DefaultLoggingConfigurator return DefaultLoggingConfigurator()
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# __init__ file import warnings warnings.filterwarnings('ignore') from . import preprocessing as pp from . import tools as tl from . import plotting as pl
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__author__ = "Simon Andermatt" __copyright__ = "Copyright (C) 2017 Simon Andermatt" from tensorflow_extra_ops.caffebicgru.caffe_c_g_r_u_step_op import *
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globalVars = {'nCells', 'nGenes', 'nNodes', 'max_node_ind', 'mem_friendly'} nCells = None nGenes = None nNodes = None max_node_ind = 0 mem_friendly = False
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from data_profiling.report.presentation.flavours.flavours import ( HTMLReport, WidgetReport, ) __all__ = [ "HTMLReport", "WidgetReport", ]
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from ._plotting import subset_for_flow_type,plot_intercellular_flows_from_inflows, plot_intercellular_flows_from_gems, plot_intercellular_flows_from_outflows
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from siapy.entities.signatures import Signals # Create from iterable (e.g. list, array) signals = Signals.from_iterable([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]])
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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.
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from pfrl.policies.deterministic_policy import * # NOQA from pfrl.policies.gaussian_policy import * # NOQA from pfrl.policies.softmax_policy import * # NOQA
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#encoding: utf-8 """Test the oddities.""" import pytest import vtkplotlib as vpl pytestmark = pytest.mark.order(4) def test_data(): vpl.data.assert_ok()
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from enum import Enum class DiscriminatorNetworks(Enum): BASELINE_DISCRIMINATOR = "baseline_discriminator" TAMING_DISCRIMINATOR = "taming_discriminator"
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from .cosformer import CosformerLayer from .performer import PerformerLayer from .transformer import VanillaTransformerLayer from .flowformer import FlowformerLayer
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from .sampler import Sampler, ExactSampler, RandomSampler from .status import SamplerStatus, Samples from .metropolis import Metropolis, LocalFlip, NeighborExchange
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from .mo import * from .graph_velocity import * from .tangent_space import * from .gam import * from .plot import * from .utils import * #import graphvelo.plot as pl
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import stanza import nltk stanza.download('en') nltk.download('wordnet') nltk.download('omw-1.4') nltk.download('punkt') nltk.download('averaged_perceptron_tagger')
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from .geometric_shapes import GeometricShapes from .shape import Shape, ShapeGeometryEnum __all__ = [ "GeometricShapes", "Shape", "ShapeGeometryEnum", ]
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"""cellnet - Scaling single cell models to bigger data sets.""" __version__ = '0.1.0' __author__ = 'Felix Fischer <felix.fischer@helmholtz-muenchen.de>' __all__ = []
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from ._network import learn_intercellular_flows from ._validate_network import apply_biological_flow, filter_low_confidence_edges, construct_intercellular_flow_network
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from .base_unet import BaseUNet from .pix2pix_unet import Pix2PixUnet from .unet_generator import UNetGenerator __all__ = ['BaseUNet', 'Pix2PixUnet', 'UNetGenerator']
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__project__ = "gtdb_to_taxdump" __author__ = "Nick Youngblut" from gtdb2td.Graph import Graph from gtdb2td import Dmp from gtdb2td import IO from gtdb2td import Utils
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from pfrl.optimizers.rmsprop_eps_inside_sqrt import RMSpropEpsInsideSqrt # noqa from pfrl.optimizers.rmsprop_eps_inside_sqrt import SharedRMSpropEpsInsideSqrt # noqa
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# -*- coding: utf-8 -*- """ Created on Mon May 9 21:16:48 2022 @author: gagan """ ROOTDIR = r'C:\Users\gagan\Research\data\Release_19AUG2020a.tar\Release_19AUG2020a'
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"""Configuration module.""" from .config import settings, APIConfig, StorageConfig, LoggingConfig __all__ = ["settings", "APIConfig", "StorageConfig", "LoggingConfig"]
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from .config_handler import YamlHandler from .base_logger import logger from .metrics import Metrics from .util import ( pad_1d_tokens, pad_2d, pad_coords, )
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import torch from torch import nn class SoftmaxCategoricalHead(nn.Module): def forward(self, logits): return torch.distributions.Categorical(logits=logits)
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# Ultralytics YOLO 🚀, GPL-3.0 license from .bot_sort import BOTSORT from .byte_tracker import BYTETracker __all__ = 'BOTSORT', 'BYTETracker' # allow simpler import
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from .datasets import * # noqa: F401,F403 from .engine import * # noqa: F401,F403 from .models import * # noqa: F401,F403 from .structures import * # noqa: F401,F403
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from pfrl.q_functions.dueling_dqn import * # NOQA from pfrl.q_functions.state_action_q_functions import * # NOQA from pfrl.q_functions.state_q_functions import * # NOQA
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"""All functionality concerned with presentation to the user.""" from data_profiling.report.structure.report import get_report_structure __all__ = ["get_report_structure"]
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from .datasets import * # noqa: F401,F403 from .evaluation import * # noqa: F401,F403 from .models import * # noqa: F401,F403 from .structures import * # noqa: F401,F403
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r"""General-purpose I/O helpers.""" from ._serialization import load, save from ._tabular import read_csv, read_table __all__ = ["read_csv", "read_table", "save", "load"]
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from ._infer_spatial import infer_spatial_information from ._ligand_receptor_database import ligand_receptor_database from ._ligand_receptor_database import filter_lr_database
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# Global variable base_url = "https://www.cbioportal.org/api" # Setting base url def configure_base_url(new_base_url=base_url): global base_url base_url = new_base_url
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#from .image import crop_center,toUint8,create_patch_image_2D #from .noise_generator import make_noise #from .norm import normalize #from .prepare import PercentileNormalizer
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import numpy as np from neuropower import peakdistribution def test_peakdistribution(): x = peakdistribution.peakdens3D(2, 1) assert np.around(x, decimals=2) == 0.48
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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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from .sites import Sites from .lattice import Lattice from .common_lattices import ( Grid, Chain, Square, Cube, Pyrochlore, Triangular, TriangularB, )