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Python
188
6
# -*- coding: utf-8 -*- from .model import VISemanticDependencyModel from .parser import VISemanticDependencyParser __all__ = ['VISemanticDependencyModel', 'VISemanticDependencyParser']
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Python
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# Copyright (c) OpenMMLab. All rights reserved. from mmcv.cnn import CONV_LAYERS from torch import nn CONV_LAYERS.register_module('Deconv', module=nn.ConvTranspose2d) # TODO: octave conv
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Python
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""" OXASL - Quantification using SVB (Stochastic VB) Copyright (c) 2008 University of Nottingham """ def run(wsp): raise NotImplementedError("SVB quantification not yet implemented")
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Python
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# -*- coding: utf-8 -*- # @Author: Sadamori Kojaku # @Date: 2023-06-15 12:07:19 # @Last Modified by: Sadamori Kojaku # @Last Modified time: 2023-06-15 12:32:16 from .generator import *
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# Settings for time-related aspects of simulation params = {} # Length of simulation before voltage is checked (ms) params["tstep"] = 100 # Size of time step (ms) params["tres"] = 0.025
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Python
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import io from PIL import Image def _plot_to_image(ploter): buf = io.BytesIO() ploter.savefig(buf, bbox_inches='tight', pad_inches=0.0) buf.seek(0) return Image.open(buf)
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Python
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""" Specification tests for HED Python library. This package contains integration tests that validate the HED Python library against the official HED specification and example datasets. """
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Python
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from .amigos import * from .deap import * from .dreamer import * from .mahnob import * from .seed import * from .seed_iv import * from .mped import * from .seed_v import * from .faced import *
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Python
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from .wgan_gp import WGANGPTrainer, CWGANGPTrainer from .beta_vae import BetaVAETrainer, CBetaVAETrainer from .ddpm import DDPMTrainer, CDDPMTrainer from .glow import GlowTrainer, CGlowTrainer
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Python
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from .config import ( CategoryConfig, ) from .stage import ( CATEGORY_COLUMN, apply_category, ) __all__ = [ "CategoryConfig", "CATEGORY_COLUMN", "apply_category", ]
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Python
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from .metabric_datasets_manager import METABRIC_Datasets_Manager from .tcga_datasets_manager import (TCGA_Balanced_Datasets_Manager, TCGA_Datasets_Manager)
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Python
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#! /usr/bin/env python # -*- coding: utf-8 -*- from main import main main(combinations=[("issue571-base", "issue571-base", "issue571-v3"), ("issue571-base", "issue571-base", "issue571-v4")])
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#! /usr/bin/env python # -*- coding: utf-8 -*- from main import main main(combinations=[("issue571-base", "issue571-base", "issue571-v6"), ("issue571-base", "issue571-base", "issue571-v7")])
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Python
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from setuptools import setup setup( name="met-brewer", version="1.0", packages=["met_brewer"], install_requires=[ "colour>=0.1.5", "matplotlib>=3.5.1" ] )
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#! /usr/bin/env python # -*- coding: utf-8 -*- from main import main main(combinations=[("issue571-base", "issue571-base", "issue571-v4"), ("issue571-base", "issue571-base", "issue571-v5")])
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#! /usr/bin/env python # -*- coding: utf-8 -*- from main import main main(combinations=[("issue571-base", "issue571-base", "issue571-v5"), ("issue571-base", "issue571-base", "issue571-v6")])
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Python
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from preprocessing import * import json import sys def main(arg): params = json.loads(arg) task = PreprocessChunkTask(**params) task.execute() if __name__ == "__main__": main(sys.argv[1])
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#! /usr/bin/env python # -*- coding: utf-8 -*- from main import main main(combinations=[("issue571-base", "issue571-base", "issue571-v2"), ("issue571-base", "issue571-base", "issue571-v3")])
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Python
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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon Jan 27 13:50:10 2020 @author: sayan """ import torch def cpu(x): x = x.to(torch.device("cpu")) return(x)
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""" https://github.com/MIPT-Oulu/Collagen/blob/master/collagen/modelzoo/segmentation/constants.py """ allowed_encoders = ['resnet18', 'resnet34', 'resnet50'] allowed_decoders = ['FPN', 'UNet']
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from .arjun_vit import ArjunViT from .simple_vit import SimpleViT from .vanilla_transformer import VanillaTransformer from .vit import ViT from .LFP_Transformer_LARGE import LFP_Transformer_LARGE
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from __future__ import absolute_import from __future__ import division from __future__ import print_function import sys from .main import main if __name__ == '__main__': sys.exit(main())
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import torch class HardTanh(torch.nn.Hardtanh): def __init__(self, min_val: float = -1, max_val: float = 1) -> None: super().__init__(min_val=min_val, max_val=max_val, inplace=False)
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"""Collection of modules for post-hoc data analysis """ from .array_ops import * from .data import * from .dynamic import * from .static import * from .statistics import * from .visualize import *
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from .create_makefile import create_makefile as create_makefile from .flatten_params import flatten_params as flatten_params from .get_vhdl_templates import get_vhdl_templates as get_vhdl_templates
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Python
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#! /usr/bin/env python # -*- coding: utf-8 -*- from main import main main(combinations=[("issue571-base", "issue571-base", "issue571-base-v2"), ("issue571-base", "issue571-base", "issue571-v2")])
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Python
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from .base_dataset import BaseDataset from .base_datasets_manager import BaseDatasetsManager from .base_model import BaseModel from .base_tester import BaseTester from .base_trainer import BaseTrainer
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from setuptools import setup, find_packages setup( name="discrete-vaes", author=["Max D Grogan"], author_email="mdg19@ic.ac.uk", version="1.0.0", packages=find_packages("tvae"), )
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print("DEPRECATION NOTICE: this module (dreamcoder.ec) will be deleted soon, " "please update your code to import from dreamcoder.dreamcoder instead") from dreamcoder.dreamcoder import * # noqa
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from utils.hgnn_utils import * from utils.data_utils import * from utils.dataloader_utils import * from utils.roi_utils import * # from utils.hyperbolic_learning_master import * print('imported utils')
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import warnings from . import train from . import losses from . import metrics warnings.warn( "`smp.utils` module is deprecated and will be removed in future releases.", DeprecationWarning, )
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__author__ = 'martinez' from .parser import Threshold from .parser import SliceSet, SynSliceSet from .parser import LinkerSet from .parser import ThresSliceSet from .parser import RwrGroup, RwrGroupSet
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# -*- coding: utf-8 -*- from .model import TetraTaggingConstituencyModel from .parser import TetraTaggingConstituencyParser __all__ = ['TetraTaggingConstituencyModel', 'TetraTaggingConstituencyParser']
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import pyqtgraph as pg import pyplot pyplot.SetJetRGBC(0.5) pyplot.SetDefScatMarkerSize(6) pg.setConfigOption('background', 'w') pg.setConfigOption('foreground', 'k') import pyclamp self = pyclamp.main()
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### configs for next hyperparam trial import optuna from datetime import datetime dataset = 'cora' task = 'lp' config = { 'set_params':{ 'task':task, ### 'dataset':dataset, } }
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import sys import pyqtgraph as pg import pyplot pyplot.SetJetRGBC(1.5) pyplot.SetDefScatMarkerSize(6) import pyclamp if __name__ == '__main__': self = pyclamp.main() sys.exit(self.App.Widget.exec_())
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from sleeplab_format.extractor import config def test_parse_config(example_extractor_config_path): cfg = config.parse_config(example_extractor_config_path) assert type(cfg) == config.DatasetConfig
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import os, gdown, tarfile def extract_tar(tar_file, dst_path, delete_after=False): tar = tarfile.open(tar_file) tar.extractall(dst_path).close() if delete_after: os.remove(tar_file)
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from .src.mac_array import mac_array as mac_array from .src.mac_bram import mac_bram as mac_bram from .src.uint_to_int import uint_to_int as uint_to_int from .utils import load_and_plugin as load_and_plugin
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from enum import Enum class Mode(Enum): """Enumeration over the different prediction modes.""" PREDICT = "predict" TRAIN = "train" def __str__(self) -> str: return str(self.value)
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""" Clustering # Author: Vladan Lucic (Max Planck Institute for Biochemistry) # $Id: __init__.py 914 2012-10-25 16:15:17Z vladan $ """ __version__ = "$Revision: 914 $" from .contingency import Contingency
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from __future__ import absolute_import from __future__ import division from __future__ import print_function from .gspan import gSpan from .config import parser from .main import main __version__ = '0.2.2'
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from typing import Any, Protocol class _NodeP(Protocol): implementation: str name: str type: str input_shape: tuple[int, int] output_shape: tuple[int, int] attributes: dict[str, Any]
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"""Parameter estimation methods for HMP models.""" from .base import BaseEstimator, EstimationResult from .em import EMEstimator __all__ = [ "BaseEstimator", "EstimationResult", "EMEstimator", ]
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from .dnn_trainer import DNN_Trainer from .multi_dnn_trainer import Multi_DNN_Trainer from .dann_trainer import DANN_Trainer from .tester import Tester from .baseline_fits_manager import Baseline_Fits_Manager
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from .coral import CORALTrainer from .ddc import DDCTrainer from .dann import DANNTrainer from .dan import DANTrainer from .ada import ADATrainer from .jan import JANTrainer from .center import CenterLossTrainer
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# -*- coding: utf-8 -*- from .model import BiaffineSemanticDependencyModel from .parser import BiaffineSemanticDependencyParser __all__ = ['BiaffineSemanticDependencyModel', 'BiaffineSemanticDependencyParser']
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""" To run snap_coordinates from your local machine, set the path to the root of mounted rhino. e.g. if rhino is mounted in a folder named RHINO in /Volumes, set RHINO_ROOT='/Volumes/RHINO' """ RHINO_ROOT='/'
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from enum import Enum class Methods(Enum): METHOD_IMG_PROC = "Image Processing" METHOD_GAN_IMG_PROC = "GAN + Image Processing" METHOD_GAN_4SM = "GAN + 4SM" METHOD_GAN_SEGMENTATION = "GAN + Segementation"
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from .src.bram_dual_port import bram_dual_port as bram_dual_port from .src.bram_single_port import bram_single_port as bram_single_port from .src.lutrom_single_port import lutrom_single_port as lutrom_single_port
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# from .expect import expect_2distr from .expect_efficient import expect_2distr from .sampling_Ustate import make_logpsi_U_afun, _logpsi_U_fun from netket.utils import _hide_submodules _hide_submodules(__name__)
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from .biotite import ( BiotiteParser, BiotiteReader, BiotiteWrapper, universe_from_atoms, ) __all__ = [ "BiotiteParser", "BiotiteReader", "BiotiteWrapper", "universe_from_atoms", ]
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import numpy as np from topaz.utils.data.coordinates import coordinates_table_to_dict, match_coordinates_to_images def test_coordinates_table_to_dict(): pass def test_match_coordinates_to_images(): pass
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# -*- coding: utf-8 -*- @profile def test_unicode(txt): # test when unicode is present txt = txt.replace (u"ی", u"ي") #Arabic Yah = ي return txt if __name__ == '__main__': test_unicode (u"ایست")
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import mmdet from packaging import version if version.parse(mmdet.__version__).major >2: from Pose_detect_mmdetect3x import Pose_detect_mmdetect else: from Pose_detect_mmdetect2x import Pose_detect_mmdetect
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import torch.nn from elasticai.creator.nn.binary.binary_quantization_function import ( Binarize as _BinarizeFn, ) class Binarize(torch.nn.Module): def forward(self, X): return _BinarizeFn.apply(X)
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### Submit script "XY.py" to HPC (Torque) cluster import subprocess subprocess.run(['echo "$PWD/extract_timeseries.py" | qsub -l nodes=1:ppn=1,walltime=48:00:00,mem=128gb -N extract_timeseries'], shell = True)
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# -*- coding: utf-8 -*- from .model import AttachJuxtaposeConstituencyModel from .parser import AttachJuxtaposeConstituencyParser __all__ = ['AttachJuxtaposeConstituencyModel', 'AttachJuxtaposeConstituencyParser']
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#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. """Add custom configs and default values""" def add_custom_config(_C): # Add your own customized configs. pass
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def aix_platform(osname, version, release): try: import _aix_support return _aix_support.aix_platform() except ImportError: pass return "{}-{}.{}".format(osname, version, release)
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from setuptools import setup with open("requirements.txt") as f: require = [x.strip() for x in f.readlines() if not x.startswith("git+")] setup( name="inv_vae", version="0.1", packages=["inv_vae"], )
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# app from .compression_based import * # noQA from .edit_based import * # noQA from .phonetic import * # noQA from .sequence_based import * # noQA from .simple import * # noQA from .token_based import * # noQA
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from __future__ import annotations import pytest from quacc.utils.kpts import bandgap_to_kspacing def test_bandgap_to_kspacing(): assert bandgap_to_kspacing(0.60) == pytest.approx(0.20040662647547047)
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from pathlib import Path import pytest @pytest.fixture def data_dir(): return Path(__file__).parent / "data" @pytest.fixture def stat_img(data_dir): return data_dir / "collection_658" / "image_10426.nii.gz"
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import pytorch_cifar_models dependencies = ['torch'] models = filter(lambda name: name.startswith("cifar"), dir(pytorch_cifar_models)) globals().update({model: getattr(pytorch_cifar_models, model) for model in models})
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"""Different methods for projecting data into subspace.""" from .base import Projector from .custom import Custom from .identity import Identity from .pca import PCA __all__ = ["Projector", "Custom", "Identity", "PCA"]
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import copy class ReplayAgent: def __init__(self, actions): self.actions = list(copy.copy(actions)) def reset(self): pass def act(self, obs, reward): return self.actions.pop(0), {}
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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon Jan 27 13:08:35 2020 @author: sayan """ import torch def gpu_t(x): x = torch.tensor(x.astype(float)).float() y = x.to("cpu") return(y)
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from cog.errors import CogError, ConfigDoesNotExist, PredictorNotSet def test_error_types_remain_importable() -> None: assert issubclass(ConfigDoesNotExist, CogError) assert issubclass(PredictorNotSet, CogError)
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"""""" class DeviceNotConnected(RuntimeError): """ Raised when the requested device is not connected. """ class IllegalSetting(ValueError): """ Raised when the device does not support the requested setting. """
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# coding: utf-8 import os.path as osp import sys def add_path(path): if path not in sys.path: sys.path.insert(0, path) this_dir = osp.dirname(__file__) lib_path = osp.join(this_dir, '.') add_path(lib_path)
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from .src import grouped_filter from .src.filter_params import FilterParameters from .src.index_generators import GroupedFilterIndexGenerator __all__ = ["FilterParameters", "grouped_filter", "GroupedFilterIndexGenerator"]
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import numpy as np def center(box: np.ndarray) \ -> np.ndarray: return np.stack(( (box[..., 2] - box[..., 0]) / 2 + box[..., 0], (box[..., 3] - box[..., 1]) / 2 + box[..., 1], ), axis=-1)
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import numpy as np from topaz.transform import ScaledGaussianMixture class TestScaledGaussianMixture(): def test_init(self): pass def test_fit(self): pass def test_transform(self): pass
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from niftymic.__about__ import ( __author__, __email__, __license__, __summary__, __version__, ) __all__ = [ "__author__", "__email__", "__license__", "__summary__", "__version__", ]
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path_to_pad = [ "/home/xtngchn3/final_submission/architecture", # todo: change path "/home/xtngchn3/final_submission/utils", # todo: change path ] data_path = "/home/xtngchn3/Dream-Challenges-Data" # todo: change path
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#!/bin/env python3 ''' Print a table of quasi-particles ''' from pdlfeynman.particle import PARTICLES print('symbol', 'name', '2Q', '2P', sep='\t') for p in PARTICLES: print(p, p.name, f'{p.Q:+2}', f'{p.P:+2}', sep='\t')
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""" Set of exceptions used in PySeg package # Author: Antonio Martinez-Sanchez (Max Planck Institute for Biochemistry) # Date: 02.04.14 """ __author__ = 'martinez' __version__ = "$Revision: 001 $" from .pexceptions import *
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# Copyright (c) OpenMMLab. All rights reserved. from mmcv.cnn import MODELS as MMCV_MODELS from mmcv.utils import Registry MODELS = Registry('model', parent=MMCV_MODELS) BACKBONES = MODELS COMPONENTS = MODELS LOSSES = MODELS
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from .ani import ANI from .miao import MiaoNet from .spinmiao import SpinMiaoNet from .miaomiao import MiaoMiaoNet from .model_interface import LitAtomicModule from .base import MultiAtomicModule from .repulsion import TwoBody
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from .file import download_url, get_package_dir_path, get_temp_dir_path from .visualize import (plot_2d_tensor, plot_3d_tensor, plot_feature_topomap, plot_raw_topomap, plot_signal, plot_adj_connectivity)
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import os import subprocess import sys argv = sys.argv argv = argv[argv.index("--") + 1 :] def main(): python = os.path.realpath(sys.executable) subprocess.run([python] + argv) if __name__ == "__main__": main()
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# Copyright (c) OpenMMLab. All rights reserved. from .cli import modify_args from .logger import get_root_logger from .setup_env import setup_multi_processes __all__ = ['get_root_logger', 'setup_multi_processes', 'modify_args']
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from .dtu import DTUDataset from .dtu_processed import DTUProcessedDataset from .kul import KULDataset from .kul_processed import KULProcessedDataset from .aved import AVEDDataset from .aved_processed import AVEDProcessedDataset
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from .ccnn import CCNN from .eegnet import EEGNet from .fbccnn import FBCCNN from .fbcnet import FBCNet from .mtcnn import MTCNN from .stnet import STNet from .tsception import TSCeption from .sst_emotion_net import SSTEmotionNet
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import autograd.numpy as np from autograd import grad def peakmem_needless_nodes(): N, M = 1000, 100 def fun(x): for i in range(M): x = x + 1 return np.sum(x) grad(fun)(np.zeros((N, N)))
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### Submit script "XY.py" to HPC (Torque) cluster import subprocess subprocess.run(['echo "$PWD/create_is-distance_matrices.py" | qsub -l nodes=1:ppn=1,walltime=01:00:00,mem=128gb -N create_distance_matrices'], shell = True)
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import setuptools __version__ = "0.1.0" setuptools.setup( name="rocf_scoring", version=__version__, python_requires=">=3.6", packages=setuptools.find_packages( exclude=["preprocessing", "scripts"] ), )
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from abc import ABC, abstractmethod import torch from elasticai.creator.vhdl.design.design import Design class DesignCreatorModule(ABC, torch.nn.Module): @abstractmethod def create_design(self, name: str) -> Design: ...
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import torch class ModuleListInfo(torch.nn.ModuleList): def __init__(self, info_str, modules=None): super().__init__(modules) self.info_str = str(info_str) def __repr__(self): return self.info_str
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"""Definition of enums.""" from enum import StrEnum class DDCMethod(StrEnum): """Enum for DDC methods.""" FIR = "finite-impulse-response-filter" AVG = "moving-average-filter" CIC = "cascaded-integrator-comb-filter"
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Python
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"""Optimizes hyperparameters using Bayesian optimization.""" from chemprop.args import HyperoptArgs from chemprop.hyperparameter_optimization import hyperopt if __name__ == "__main__": hyperopt(args=HyperoptArgs().parse_args())
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from . import config from . import common, mesh, model, model_assembly, solvers, units, mesh_tools __all__ = [ "config", "model_assembly", "solvers", "model", "common", "mesh", "units", "mesh_tools", ]
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from cuppa.runners.args import RunnerArgParser from cuppa.runners.training_runner import TrainingRunner if __name__ == "__main__": kwargs = RunnerArgParser().get_kwargs_train() runner = TrainingRunner(**kwargs) runner.run()
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from setuptools import setup, find_packages setup( name="hippo", version="1.0", description="Hippocampus models", author="Toon Van de Maele", author_email="toonvandemaele@gmail.com", packages=find_packages(), )
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from dash import html, get_app, register_page register_page(__name__, path='/', description="Home") layout = html.Div( [ html.H3([ "Project description", ]), html.Hr(), ] ) app = get_app()
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from wikibaseintegrator.datatypes.string import String class ExternalID(String): """ Implements the Wikibase data type 'external-id' """ DTYPE = 'external-id' PTYPE = 'http://wikiba.se/ontology#ExternalId'
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import pandas as pd file_paths = ['Barklabel.csv','Howllabel.csv'] # Tüm uçuşlara ait verileri birleştirme all_data = pd.concat([pd.read_csv(file) for file in file_paths]) all_data.to_csv('DogKaggle.csv', index=False)
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""" Set of functions for IO communication with DisPerSe # Author: Antonio Martinez-Sanchez (Max Planck Institute for Biochemistry) # Date: 02.04.14 """ __author__ = 'martinez' __version__ = "$Revision: 001 $" from .disperse_io import *
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from .aseinterface import * try: from .sharcinterface import * except ModuleNotFoundError: import logging log = logging.getLogger(__name__) log.warning("PySHARC not installed! Install PySHARC to use all features of SPaiNN")