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Python
170
9
''' Author: ---- Date: 2022-04-04 23:33:18 LastEditors: GhMa LastEditTime: 2022-10-02 19:28:52 ''' from .snn import * from .snn_modules import * from .toy_net import *
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Python
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# -*- coding: utf-8 -*- """ Created on Tue Jun 8 12:53:54 2021 @author: fhu14 """ from .skfinfo import SkfInfo from .skfwriter import main, determine_index, write_skfs
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Python
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from metric.surface_distance_avg import surface_distance_calc def surface_distance_cat4(output, target, misc): return surface_distance_calc(output, target, misc, 4)
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Python
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from metric.surface_distance_avg import surface_distance_calc def surface_distance_cat3(output, target, misc): return surface_distance_calc(output, target, misc, 3)
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Python
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from metric.surface_distance_avg import surface_distance_calc def surface_distance_cat5(output, target, misc): return surface_distance_calc(output, target, misc, 5)
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Python
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from .common.get_model import get_model from .common.get_optimizer import get_optimizer from .common.get_scheduler import get_scheduler from .common.utils import get_unit
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Python
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from metric.surface_distance_avg import surface_distance_calc def surface_distance_cat7(output, target, misc): return surface_distance_calc(output, target, misc, 7)
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Python
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from metric.surface_distance_avg import surface_distance_calc def surface_distance_cat6(output, target, misc): return surface_distance_calc(output, target, misc, 6)
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Python
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from metric.surface_distance_avg import surface_distance_calc def surface_distance_cat2(output, target, misc): return surface_distance_calc(output, target, misc, 2)
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Python
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from metric.surface_distance_avg import surface_distance_calc def surface_distance_cat1(output, target, misc): return surface_distance_calc(output, target, misc, 1)
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from importlib.metadata import version, PackageNotFoundError try: __version__ = version("torchmd") except PackageNotFoundError: # package is not installed pass
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Python
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#!usr/bin/env python #coding:utf-8 """ @author: Haidong Zhang @contact: haidong_zhang14@yahoo.com @time: 2020/12/10 17:12 @project: MalariaDetection @description: """
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Python
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#!usr/bin/env python #coding:utf-8 """ @author: Haidong Zhang @contact: haidong_zhang14@yahoo.com @time: 2020/12/14 22:17 @project: MalariaDetection @description: """
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Python
175
9
#!usr/bin/env python #coding:utf-8 """ @author: Haidong Zhang @contact: haidong_zhang14@yahoo.com @time: 2020/12/10 16:00 @project: MalariaDetection @description: """
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Python
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from loss.bce_loss import bce_loss from loss.dice_loss import dice_loss def bce_and_dice_loss(output, target): return bce_loss(output, target) + dice_loss(output, target)
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from importlib.metadata import version, PackageNotFoundError try: __version__ = version("torchmd-net") except PackageNotFoundError: # package is not installed pass
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Python
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ls = LeafSupport('../input.nex') #Setting the proportion of quartets or triplets to sample, range 0.0 - 1.0 ls.useAllQuartets = False ls.noQuartetsToUse=0.4 ls.leafSupport()
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Python
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# Copyright (C) 2022-2025 ETH Zurich, Niels Maeder and other DASH contributors. from .explainer import Explainer from .extractor import Extractor __all__ = [ Extractor, ]
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Python
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var.doCheckForBlankSequences=False var.verboseRead = 0 read('d.nex') d = Data() read('opt.p4_tPickle') t = var.trees[0] t.data = d t.simsForModelFitTests(reps=11) t.modelFitTests()
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Python
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# -*- coding: utf-8 -*- """ Created on Tue Jun 8 13:01:15 2021 @author: fhu14 """ from .rotations import get_axis_rotation_matrix, rotation_matrix_atom, rotation_matrix_bond
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Python
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10
cpr = McmcCheckPointReader(theGlob='*') cpr.writeProposalAcceptances() # How was swapping between heated chains? cpr.writeSwapVectors() if 0: m = cpr.mm[0] print(m.tunings)
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Python
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# Copyright (C) 2024-2025 ETH Zurich, Niels Maeder and other DASH contributors. from .cleaner import CLEANER_CONTENT from .worker import get_lsf_worker_content, get_slurm_worker_content
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"""Utilities for reading and writing Evolutionary Compression (.ecomp) archives.""" from .reader import read_ecomp from .writer import write_ecomp __all__ = ["read_ecomp", "write_ecomp"]
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10
from metric.hausdorff_distance95_avg import hausdorff_distance95_calc def hausdorff_distance95_cat5(output, target, misc): return hausdorff_distance95_calc(output, target, misc, 5)
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Python
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from metric.hausdorff_distance95_avg import hausdorff_distance95_calc def hausdorff_distance95_cat7(output, target, misc): return hausdorff_distance95_calc(output, target, misc, 7)
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from .adversarial_loss import * # NOQA from .duration_prediction_loss import * # NOQA from .feat_match_loss import * # NOQA from .mel_loss import * # NOQA from .stft_loss import * # NOQA
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Python
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from metric.hausdorff_distance95_avg import hausdorff_distance95_calc def hausdorff_distance95_cat4(output, target, misc): return hausdorff_distance95_calc(output, target, misc, 4)
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Python
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from metric.hausdorff_distance95_avg import hausdorff_distance95_calc def hausdorff_distance95_cat6(output, target, misc): return hausdorff_distance95_calc(output, target, misc, 6)
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Python
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from metric.hausdorff_distance95_avg import hausdorff_distance95_calc def hausdorff_distance95_cat3(output, target, misc): return hausdorff_distance95_calc(output, target, misc, 3)
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Python
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from metric.hausdorff_distance95_avg import hausdorff_distance95_calc def hausdorff_distance95_cat2(output, target, misc): return hausdorff_distance95_calc(output, target, misc, 2)
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Python
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import logging import sys logging.basicConfig(stream=sys.stdout, level=logging.INFO, format="%(message)s") logger = logging.getLogger(__name__) log_file_logger = logging.getLogger("log_file")
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Python
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from metric.hausdorff_distance95_avg import hausdorff_distance95_calc def hausdorff_distance95_cat1(output, target, misc): return hausdorff_distance95_calc(output, target, misc, 1)
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import yaml def load_yaml(filename): with open(filename, 'r') as stream: try: return yaml.safe_load(stream) except yaml.YAMLError as exc: print(exc)
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Python
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tp = TreePartitions('mcmc_trees_0.nex', skip=1000) t = tp.consensus() for n in t.iterInternalsNoRoot(): n.name = '%.0f' % (100.0 * n.br.support) t.write() t.draw(width=30, showNodeNums=0)
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Python
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# from . import bigquery # from . import pandas # from . import polars # from . import spark # from .pandas import mrmr_classif, mrmr_regression # from .main import mrmr_base __version__ = "0.2.8"
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Python
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from .hifigan import * # NOQA from .melgan import * # NOQA from .parallel_wavegan import * # NOQA from .style_melgan import * # NOQA from .uhifigan import * # NOQA from .vqvae import * # NOQA
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Python
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# Copyright (c) OpenMMLab. All rights reserved. from mmcv.utils import Registry FILTERS = Registry('filters') def build_filter(cfg): """Build filters function.""" return FILTERS.build(cfg)
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# Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org # Distributed under the MIT License. # (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
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Python
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############### Configuration file for Training of SplitMNIST and Mixtures ############### n_epochs = 5 lr_start = 0.001 num_workers = 4 valid_size = 0.2 batch_size = 256 train_ens = 15 valid_ens = 10
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from os.path import dirname, basename, isfile import glob modules = glob.glob(dirname(__file__) + "/*.py") __all__ = [basename(f)[:-3] for f in modules if isfile(f) and not f.endswith('__init__.py')]
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from .zoo import get_model # noqa: F401 from .weights_fetcher import fetch_weights # noqa: F401 from .git_cloner import GitCloneError # noqa: F401 from .model_loader import ModelLoader # noqa: F401
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var.doCheckForDuplicateSequences=False read('d.nex') a=var.alignments[0] a.setCharPartition('cp1') d = Data() d.alignments[0].writePhylip() oneBoot = d.bootstrap() oneBoot.alignments[0].writePhylip()
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from . import particle from . import population from .algorithm import psoSimple, vlpso from .report import PSOReport from .callback import Callback, EarlyStopping from .util import rankFeatures
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# Copyright (c) OpenMMLab. All rights reserved. from .utils import check_result_same, construct_toy_data, create_random_bboxes __all__ = ['create_random_bboxes', 'construct_toy_data', 'check_result_same']
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from .binn import BINN from .logger import SuperLogger from .network import Network from .importance_network import ImportanceNetwork from .sklearn import BINNClassifier from .explainer import BINNExplainer
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# -*- coding: utf-8 -*- """ Created on Mon Jun 7 20:02:31 2021 @author: fhu14 """ from .dftb import DFTB, ANGSTROM2BOHR from .fermilevel import determine_fermi_level, fermi from .sccparam import _Gamma12
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# Copyright (c) OpenMMLab. All rights reserved. from .base_panoptic_fusion_head import \ BasePanopticFusionHead # noqa: F401,F403 from .heuristic_fusion_head import HeuristicFusionHead # noqa: F401,F403
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from .bibiplot import create_bibiplot1x2 from .alignmentplot import plot_alignment, plot_alignment_error, plot_alignment_and_error from .scatter import scatter2d, scatter3d from .heatmap import create_heatmap2
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var.doCheckForDuplicateSequences = False read('ds.nex') a=var.alignments[0] a.writePhylip(None) b = a.subsetUsingCharSet('cs2') b.writePhylip(None) if 0: # Turn on to see some details a.nexusSets.dump()
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from setuptools import find_packages, setup setup( name='dtor', packages=find_packages(), version='0.1.0', description='Deep-learning response prediction', author='SB-MT', license='MIT', )
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# Read in the last one ... cpr = McmcCheckPointReader(theGlob='*', last=True) m = cpr.mm[0] t = m.treePartitions.consensus() for n in t.iterInternalsNoRoot(): n.name = "%.0f" % (n.br.support * 100) t.draw()
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Python
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# -*- coding: utf-8 -*- """ Created on Fri Jun 11 19:42:58 2021 @author: fhu14 Module for handling predictions for targets generated from the dftb_layer """ from .molec_target_updates import organize_predictions
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# This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed # under the Apache License Version 2.0, see <https://www.apache.org/licenses/> from sbi.examples.minimal import flexible, simple
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#!/usr/bin/env python # -*- coding: utf-8 -*- """Convenience wrapper for running clipkit directly from source tree.""" import sys from clipkit.clipkit import main if __name__ == "__main__": main(sys.argv[1:])
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# -*- coding: utf-8 -*- """ Created on Fri Jun 4 16:15:20 2021 @author: fhu14 """ from .input_parser import parse_input_dictionaries, construct_final_settings_dict,\ collapse_to_master_settings, inflate_to_dict
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var.verboseRead = 0 read('../../noTRuberNoGapsNoAmbiguities.nex') d = Data() read('opt.p4_tPickle') t = var.trees[0] t.data = d t.compoTestUsingSimulations(nSims=100, doIndividualSequences=0, doChiSquare=1, verbose=1)
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# This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed # under the Apache License Version 2.0, see <https://www.apache.org/licenses/> from sbi.__version__ import __version__ # noqa: F401
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# Make a consensus tree, uncomplicated. tp = TreePartitions("mcmc_trees_0.nex", skip=500) t = tp.consensus() for n in t.iterInternalsNoRoot(): n.name = "%.0f" % (100. * n.br.support) t.draw() t.writeNexus('cons.nex')
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# Copyright (c) OpenMMLab. All rights reserved. from .builder import build_iou_calculator from .iou2d_calculator import BboxOverlaps2D, bbox_overlaps __all__ = ['build_iou_calculator', 'BboxOverlaps2D', 'bbox_overlaps']
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# Copyright (C) 2022-2025 ETH Zurich, Niels Maeder and other DASH contributors. """Typing tools for the use of rdkit.""" from rdkit import Chem Molecule = Chem.rdchem.Mol Atom = Chem.rdchem.Atom Bond = Chem.rdchem.Bond
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from rdkit import Chem def make_mol(s: str): """ Builds an RDKit molecule from a SMILES string. :param s: SMILES string. :return: RDKit molecule. """ mol = Chem.MolFromSmiles(s) return mol
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# analytical corrections from .zbl_repulsion_energy import ZBLRepulsionEnergy from .electrostatic_energy import ElectrostaticEnergy from .d4_dispersion_energy import D4DispersionEnergy from .functional import cutoff_function
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from .noise import plot_gain_fit, estimate_gain import logging log = logging.getLogger(__name__) try: from .deconvolution import deconvolve except ModuleNotFoundError: log.warn("deconvolution module not available")
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# Variable that modifies output aspects depending on whether the code is to be executed on a notebook or on a # terminal. By default it is assumed that it will be executed on a terminal. USE_NOTEBOOK_ENVIRONMENT = False
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from p4.supertreesupport import SuperTreeSupport sts = SuperTreeSupport('supertree.nex', 'input.nex') sts.doSaveDecoratedTree = False sts.decoratedFilename='mytree.nex' sts.verbose=2 sts.doDrawTree=True sts.superTreeSupport()
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# This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed # under the Apache License Version 2.0, see <https://www.apache.org/licenses/> VERSION = (0, 27, 0) __version__ = ".".join(map(str, VERSION))
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# Copyright (c) OpenMMLab. All rights reserved. from .camera_base import CAMERAS from .single_camera import SimpleCamera from .single_camera_torch import SimpleCameraTorch __all__ = ['CAMERAS', 'SimpleCamera', 'SimpleCameraTorch']
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# Copyright (c) OpenMMLab. All rights reserved. from .image import (color_val_matplotlib, imshow_det_bboxes, imshow_gt_det_bboxes) __all__ = ['imshow_det_bboxes', 'imshow_gt_det_bboxes', 'color_val_matplotlib']
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from dtor.datasets.dataset_nominal import CTImageDataset dset = CTImageDataset("data/external/Label.csv", label="L_LTP_date", shape=(64, 64, 64), stride=(32, 32, 32)) # shape=(64,64,48), stride=(32, 32, 24))
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# Copyright (c) OpenMMLab. All rights reserved. from .transforms import (bbox_cs2xywh, bbox_xywh2cs, bbox_xywh2xyxy, bbox_xyxy2xywh) __all__ = ['bbox_xywh2xyxy', 'bbox_xyxy2xywh', 'bbox_xywh2cs', 'bbox_cs2xywh']
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# This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed # under the Apache License Version 2.0, see <https://www.apache.org/licenses/> from sbi.inference.trainers.marginal.marginal_base import MarginalTrainer
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import numpy as np import random # from theano.tensor.shared_randomstreams import RandomStreams def set_random_seeds(random_seed): # srng = RandomStreams(seed=random_seed) random.seed(random_seed) np.random.seed(random_seed)
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"""Command modules for the beast CLI.""" from daart.cli.commands import predict, train # dictionary of all available commands COMMANDS = { 'train': train, # model training 'predict': predict, # model inference on keypoints }
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"""Functions for reading and writing files.""" import yaml def load_yaml(filename): with open(filename, "r") as stream: try: return yaml.safe_load(stream) except yaml.YAMLError as exc: print(exc)
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# Copyright (c) OpenMMLab. All rights reserved. from .monitor_node import MonitorNode from .object_assigner_node import ObjectAssignerNode from .recorder_node import RecorderNode __all__ = ['MonitorNode', 'ObjectAssignerNode', 'RecorderNode']
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from .util import normalize, rotx, roty, rotz, get_rgba, translate, scale from .shapes import GlQuad, GlTri, GlVertices, GlCube, GlSphericalRect, GlCylindricalWithPhiRect, GlSphericalCirc, GlCylinder, GlSphericalPoints, GlSphericalTexturedRect
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ls = LeafSupport('../input.nex') #Set writeCsv to true to output the resulting lists to a csv file. ls.writeCsv = False #The csv filename can be specified, default name is leafSupport.csv ls.csvFilename='leafSupport.csv' ls.leafSupport()
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"""Useful utils """ from .misc import * from .logger import * from .visualize import * from .eval import * # progress bar import os, sys sys.path.append(os.path.join(os.path.dirname(__file__), "progress")) # from progress.bar import Bar as Bar
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from p4.supertreesupport import SuperTreeInputTrees stit = SuperTreeInputTrees('balanced64.nex') stit.writeInputTreesToFile = True stit.outputFile = 'inputtrees.tre' stit.noTaxaToRemove = 32 stit.noOutputTrees = 20 stit.generateInputTrees()
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var.doCheckForDuplicateSequences = False var.verboseRead = 0 read('ds.nex') b = var.alignments[1] b.writePhylip() b.excludeCharSet('cs2') b.writePhylip() b.setCharPartition('cD') d = Data() d.dump() b.setCharPartition(None) d = Data() d.dump()
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var.verboseRead = 0 read('d.nex') d = Data() d.compoChiSquaredTest(verbose=1, skipColumnZeros=1, useConstantSites=1, skipTaxNums=None, getRows=0) read('opt.p4_tPickle') t = var.trees[0] t.data = d t.simsForModelFitTests(reps=83) t.modelFitTests()
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import os def create_folders(path): # create output folder if it does not exist if (path != '') and (not os.path.isdir(path)): print(f'directory "{path}" does not exist and will be created.') os.makedirs(path, exist_ok=True)
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# -*- coding: utf-8 -*- """ Created on Mon Jun 7 18:31:26 2021 @author: fhu14 """ from .charge_loss import ChargeLoss from .dipole_loss import DipoleLoss from .form_penalty_loss import FormPenaltyLoss from .total_energy_loss import TotalEnergyLoss
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tp = TreePartitions("mcmc_trees_0.nex", skip=200) t = tp.consensus() # put support on node.name's, for the text drawing for n in t.iterInternalsNoRoot(): n.name = "%.0f" % (100. * n.br.support) t.draw() # Save it t.writeNexus(fName='cons.nex')
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import json from glob import glob from FOD.Predictor import Predictor with open('config.json', 'r') as f: config = json.load(f) input_images = glob('input/*.jpg') + glob('input/*.png') predictor = Predictor(config, input_images) predictor.run()
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# All dataset training Neuron Segmentation nohup python tools/train.py configs/swin-sim-seg/1_mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_sim_seg.py --gpu-ids 0 --work-dir results_all_data/09-19-2022/ > results_all_data/09-19-2022/output_all_data_seg.log &
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import sys from os.path import join, dirname, realpath, exists from os import makedirs current_dir = dirname(realpath(__file__)) sys.path.insert(0, dirname(current_dir)) # from config_path import PLOTS_PATH # saving_dir = join(PLOTS_PATH, 'analysis')
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# Copyright (c) OpenMMLab. All rights reserved. from .coco_api import COCO, COCOeval from .panoptic_evaluation import pq_compute_multi_core, pq_compute_single_core __all__ = [ 'COCO', 'COCOeval', 'pq_compute_multi_core', 'pq_compute_single_core' ]
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import socket def find_random_open_port() -> int: """Find a random open port on localhost.""" with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind(("", 0)) port = s.getsockname()[1] return port # noqa: RET504
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# Copyright (c) OpenMMLab. All rights reserved. from ..builder import DETECTORS from .detr import DETR @DETECTORS.register_module() class DeformableDETR(DETR): def __init__(self, *args, **kwargs): super(DETR, self).__init__(*args, **kwargs)
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from p4.supertreesupport import SuperTreeInputTrees stit = SuperTreeInputTrees('balanced64.nex') stit.writeInputTreesToFile = True stit.outputFile = 'inputtreesWithDist.tre' stit.useTaxonDistribution = True stit.noOutputTrees = 20 stit.generateInputTrees()
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# Copyright (c) OpenMMLab. All rights reserved. from .fpn import FPN from .gap_neck import GlobalAveragePooling from .posewarper_neck import PoseWarperNeck from .tcformer_mta_neck import MTA __all__ = ['GlobalAveragePooling', 'PoseWarperNeck', 'FPN', 'MTA']
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import torch def accuracy_binary(output, target): with torch.no_grad(): pred = (output > 0.5) assert pred.shape[0] == len(target) correct = 0 correct += torch.sum(pred == target).item() return correct / target.numel()
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rNum = 0 tp = TreePartitions('mcmc_trees_%i.nex' % rNum, skip=100) t = tp.consensus() # Transfer node.br.support to node.name as percent for n in t.iterInternalsNoRoot(): n.name = "%.0f" % (100. * n.br.support) t.draw() t.writeNexus("cons%i.nex" % rNum)
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from .fitting import * from .statistics import * from .distributions import * from .data import * try: from ._version import __version__ except ImportError: # Fallback for development installations without setuptools_scm __version__ = "0.0.0.dev0"
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import setuptools setuptools.setup( name="pybrid", version="0.0.1", description=("Hybrid inference: Inferring fast and slow"), license="MIT License", url="https://github.com/alec-tschantz/pybrid/", packages=["pybrid", "pybrid.models"], )
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# This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed # under the Apache License Version 2.0, see <https://www.apache.org/licenses/> from sbi.samplers.rejection.rejection import ( accept_reject_sample, rejection_sample, )
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from p4.supertreesupport import SuperTreeInputTrees stit = SuperTreeInputTrees('balanced64.nex', distributionTrees='FelidaeRVS.tre') stit.writeInputTreesToFile = True stit.outputFile = 'inputtreesBuiltDist.tre' stit.noOutputTrees = 20 stit.generateInputTrees()
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from .BBB.BBBLinear import BBBLinear as BBB_Linear from .BBB.BBBConv import BBBConv2d as BBB_Conv2d from .BBB_LRT.BBBLinear import BBBLinear as BBB_LRT_Linear from .BBB_LRT.BBBConv import BBBConv2d as BBB_LRT_Conv2d from .misc import FlattenLayer, ModuleWrapper
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tp = TreePartitions("mcmc_trees_0.nex", skip=500) tp.read("mcmc_trees_1.nex", skip=500) t = tp.consensus(minimumProportion=0.5) for n in t.iterInternalsNoRoot(): n.name = "%.0f" % (100. * n.br.support) t.draw() t.name = 'stMcmc' t.writeNexus('stMcmcCons.nex')