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from .bmesh_face_ops import * from .bmesh_object_ops import * from .bmesh_vertex_ops import *
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from backend.settings import ensure_pretrained_models ensure_pretrained_models() print('Done')
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CURIE = str LABEL = str URI = str PRED_CURIE = CURIE SUBSET_CURIE = CURIE TAXON_CURIE = CURIE
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"""Realign the Kaldi generated CTMs and segments to the transcript to recover speaker info."""
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from ppqm.utils.files import WorkDir from ppqm.utils.functools import exit_after, func_parallel
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from main import app # do some production specific things to the app app.config['DEBUG'] = False
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from oaklib.datamodels.search import SearchConfiguration SEARCH_CONFIG = SearchConfiguration()
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r""" instruments is an useful additional functions and methods for analyzing spike networks """
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from ._boosting import boosting, BoostingResult from ._predictors import epoch_impulse_predictor
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from byol_pytorch.byol_pytorch import BYOL from byol_pytorch.byol_pytorch_get_feature import BYOL
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from .image_pipeline import ImagePipeline #from .image_pipeline_widget import ImagePipelineWidget
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from .loss import * from .optimizer import build_optimizer from .scheduler import build_scheduler
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import torch from .nx_conversion import to_networkx_conv, whole_graph_mask_to_subgraph, mask_graph
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from .math_phase import * from .math_lattice import * from .my_io import * from .my_logging import *
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. # from .rpn import build_rpn
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# # Copied From [mmdetection](https://github.com/open-mmlab/mmdetection/tree/master/mmdet/ops/dcn) #
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# content of test_sample.py def func(x): return x + 1 def test_answer(): assert func(5) == 6
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. from .defaults import _C as cfg
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# import demo from CoBeL-RL import cobel.demo.sfma.sfma_demo as demo # run SFMA demo demo.single_run()
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from .sequence_explorer import * from .frag_sequence_explorer import * from .frag_graph_explorer import *
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from abc import ABC from dataclasses import dataclass @dataclass class Reasoner(ABC): # noqa pass
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from FBEM.run import * from FBEM.postproc import * from FBEM.write_input import * import FBEM.logs as logs
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def test_resources(folder_in): assert (folder_in / "E301_FLUT_AC1_combined_ID_cleared.nii").is_file()
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### version 1.1.7 ### import os os.system('nrnivmodl') from neuron import h, gui h.load_file("nrnez.hoc")
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import torch class Squeezer(torch.nn.Module): def forward(self, x): return x.squeeze(dim=-1)
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from .ubergraph_implementation import UbergraphImplementation __all__ = [ "UbergraphImplementation", ]
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. from .build import make_data_loader
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from .deep_sleep_net import DeepFeatureNet, DeepSleepNet from .utime import UTime from .usleep import USleep
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from .modules import * from .parallel import UserScatteredDataParallel, user_scattered_collate, async_copy_to
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from .environ import get_environment_variables from .io import FIG_PATH, OUT_PATH, get_out_dir, glue, savefig
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from .attention import * from .encoderdecoder import * from .transform import * from .single_network import *
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""" Set of auxiliary functions for RACCOON F. Comitani @2020-2022 """ if __name__ == "__main__": pass
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""" Set of standardized tests for RACCOON F. Comitani @2020-2021 """ if __name__ == "__main__": pass
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from .file import SegmentedFile, ColocalisedFile, File from .colocalisation_result import ColocalisationResult
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from __future__ import absolute_import from __future__ import division from __future__ import print_function
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from oaklib.mappers.ontology_metadata_mapper import OntologyMetadataMapper __all__ = ["OntologyMetadataMapper"]
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from mesh.plot.mlab_viewer import MeshViewer mv = MeshViewer('data/sphere_wall') mv.triangular_mesh() mv.show()
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"""mb_dual_dan: dual-pathway dopaminergic model of the larval Drosophila mushroom body.""" __version__ = "0.1.0"
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"""Useful utils """ from .misc import * from .logger import * from .visualize import * from .model_utils import *
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""" Set of optimization functions for RACCOON F. Comitani @2020-2021 """ if __name__ == "__main__": pass
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from .structured_feature import make_structured_feature from .network_statistics import make_network_stats_feature
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. from .detectors import build_detection_model
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from .ModelNet40 import ModelNet40, ModelNet40Attack, \ ModelNet40Normal, ModelNet40NormalAttack, ModelNet40Hybrid
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import numpy as np import sys from modules.parameters import ParameterSet, copy_dict from defaults.paths import paths
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from .resnet import ResNet, resnet18, resnet34, resnet50, resnet101, resnet152 from .xception import Xception, xception39
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from utils.utils import * from utils.kNN import embedding_matrix_2_kNN, knn_similarities from utils.Config import Config
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# This file holding some environment constant for sharing by other files import torch TORCH_VERSION = torch.__version__
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"""Write changes in markdown format to a file.""" from .change_handler import ChangeHandler __all__ = ["ChangeHandler"]
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from .centerCropCommon import center_crop class negativeCenterC: def negativeCenter(self): center_crop(self)
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import pdoc s = pdoc.html('bayesian_bootstrap.bootstrap') with open('bootstrap_documentation.html', 'w') as f: f.write(s)
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"""Trains a model on a dataset.""" from chemprop.train import chemprop_train if __name__ == '__main__': chemprop_train()
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# Load subpackages in current namespace from .pycpd import pycpd from . import cutting from . import model from . import utils
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class RelationshipCollection: def __init__(self): self.DirectRelationships = [] self.GapRelationships = []
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from cmseq.cmseq import CMSEQ_DEFAULTS from cmseq.cmseq import BamFile from cmseq.cmseq import BamContig __version__ = '1.0.4'
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from .mobilenet import * from .mobilenetv2 import * from .mobilenetv3 import * from .igcv3 import * from .efficientnet import *
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. from .backbone import build_backbone from . import fbnet
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from .sequence_rnn import * from .sequence_transformer import SequenceTransformer from .graph_transformer import GraphTransformer
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import numpy as np x = np.random.rand(100) y = np.random.rand(100) sort_inds = np.argsort(x) x = x[sort_inds] y = y[sort_inds]
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from .define import * from .operate import * from mesh.shapes.Regions2D import * from .convert import fuse_mesh, triangulate_system
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# cfg.py global sampleName global outfilepath global logFile global logOpen global home global run_clipper global verbose
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from oaklib.datamodels.search import SearchConfiguration __all__ = [ "SEARCH_CONFIG", ] SEARCH_CONFIG = SearchConfiguration()
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# Load subpackages in current namespace from . import cv from . import deeplearning from . import synthetic from . import clustering
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# Copyright (c) Open-MMLab. All rights reserved. from .quantization import dequantize, quantize __all__ = ['quantize', 'dequantize']
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import sys from ..common.base import serve from .worker import MockWorker, build_parser sys.exit(serve(MockWorker, build_parser()))
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def subset_to_shorthand(subset: str) -> str: if "#" in subset: return subset.split("#")[-1] else: return subset
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from __future__ import absolute_import from __future__ import division from __future__ import print_function from tflib.ops.ops import *
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# import demo from CoBeL-RL (requires Tensorflow!) import cobel.demo.sfma.deep_sfma_demo as demo # run Deep SFMA demo demo.single_run()
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from .flow import ( estimate_spring_rank_P, signal_flow, rank_signal_flow, rank_graph_match_flow, calculate_p_upper, )
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import sys from ..common.base import serve from .worker import Cosmos3Worker, build_parser sys.exit(serve(Cosmos3Worker, build_parser()))
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""" Guide 02 ========================= Define your own algorithm and/or metrics. - It needs... fit? (empty) - It needs... transform? """
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import torch from graphxai.datasets import Benzene dataset = Benzene(split_sizes = (0.7, 0.2, 0.1), seed = 1234) print(dataset.test_index)
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from __future__ import absolute_import from __future__ import division from __future__ import print_function from tflib.ops.layers import *
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axis_heading = { 'fontname':'Arial', 'fontsize': 8 } axis_label = { 'fontname':'Arial', 'fontsize': 7 }
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import unittest class TestEmpty(unittest.TestCase): def test_empty(self): pass if __name__ == "__main__": unittest.main()
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import modeltest.rnn.pretrain as pretrain import modeltest.rnn.processing as processing def test(): processing.main() pretrain.main()
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from __future__ import absolute_import from __future__ import division from __future__ import print_function from tflib.layers.layers import *
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# Load subpackages in current namespace from . import detection from . import GUI from . import tracking from . import utils from . import main
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from __future__ import absolute_import from __future__ import division from __future__ import print_function from tflib.vision.dataset import *
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import sys from ..common.base import serve from .worker import Transfer25Worker, build_parser sys.exit(serve(Transfer25Worker, build_parser()))
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import modeltest.graph.pretrain as pretrain import modeltest.graph.processing as processing def test(): processing.main() pretrain.main()
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# _version.py file will automatically be generated by setuptools_scm when publishing a release from ._version import __version__, __version_tuple__
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from setuptools import setup setup( name='EphysAFM', version='0.0.1', description='Analysis', py_modules=[""], package_dir={'': 'src'} )
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from utils.postprocessing.PostProcessing import PostProcessing from utils.postprocessing.SelectLabelsPostprocessor import SelectLabelsPostprocessor
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import sys from ..common.base import serve from .worker import WsmRendererWorker, build_parser sys.exit(serve(WsmRendererWorker, build_parser()))
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# GENERATED VERSION FILE # TIME: Wed Jul 29 13:01:29 2020 __version__ = '2.3.0rc0+8194b16' short_version = '2.3.0rc0' version_info = (2, 3, "0rc0")
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from .gratin import Gratin, GratinParameters from .density import Density from .diffusivity import StepsAndDiffusion from .tag_cells import TagCells
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from .arcface_models import ArcMarginModel from .arcface_models import ResNet from .arcface_models import IRBlock from .arcface_models import SEBlock
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import sys from ..common.base import serve from .worker import Transfer25AVWorker, build_parser sys.exit(serve(Transfer25AVWorker, build_parser()))
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from __future__ import absolute_import from __future__ import division from __future__ import print_function from tflib.vision.dataset.mnist import *
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from abc import ABCMeta, abstractmethod class ContextBase(metaclass = ABCMeta): @property @abstractmethod def set_context(): pass
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from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import time import logging
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from .Perturb import CWPerturb from .Add import CWAdd from .kNN import CWKNN from .Add_Cluster import CWAddClusters from .Add_Objects import CWAddObjects
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import modeltest.seq_trans.pretrain as pretrain import modeltest.seq_trans.processing as processing def test(): processing.main() pretrain.main()
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from .perturb import ( remove_edges, add_edges, shuffle_edges, remove_edges_subgraph, add_edges_subgraph, shuffle_edges_subgraph, )
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from setuptools import setup, find_packages setup( name='tctx', version='1.0', description='Turtle cortex model', packages=find_packages() )
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""" Oaklib ------ """ __version__ = '0.1.0' from oaklib.interfaces import BasicOntologyInterface from oaklib.resource import OntologyResource schemes = {}
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from .model import MoleculeModel from .mpn import MPN, MPNEncoder __all__ = [ 'MoleculeModel', 'MPN', 'MPNEncoder' ] def Beaf(): return None
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from config.config import * from process.dataloader import * from process.augmentation import * from process.embed_gen import * from process.evaluate import *
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import matplotlib.pyplot as plt from mesh.plot.mpl_viewer import MeshViewer mv = MeshViewer('data/sphere_wall') mv.create_ax() mv.triangular_mesh() mv.show()
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collect_ignore = [ "InterfaceResidues.py", "Unipartite.py", "show_contacts.py", "focal_blur.py", "data2bfactor.py", "spectrumany.py", ]