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import torch from torch.nn.functional import softmax def rmse(outputs, target): return torch.sqrt(torch.mean((softmax(outputs, dim=0)-target)**2))
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""" This module contains Spitfire's analytical Jacobian and numerical methods 'engine' named griffon, which is written in C++ and wrapped by Cython """
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import torch from torch.nn import functional def softmax(x): return functional.softmax(x, dim=-1) def swish(x): return x * torch.sigmoid(x)
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from .config import Config from .engine import Engine from .store import Store from . import metrics __all__ = ["Config", "Engine", "Store", "metrics"]
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import timm def load_pretrained_encoder(model_name="resnet18", num_classes=2): return timm.create_model(model_name, pretrained=True, num_classes=2)
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from .spatials import clustering from .spatials import smooth from .spatials import trajectory from .spatials import morphology from .spatials import SME
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# ruff: noqa: F401 from ._json import ( to_json, write_json, from_json, read_json, config, to_json_plotly, from_json_plotly, )
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from .data_split import DataSplit from .molecule_split import MoleculeSplit from .random_split import RandomSplit from .scaffold_split import ScaffoldSplit
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import json def test_info(client): response = client.get('/api/info/') data = json.loads(response.data) assert data.get("git") is not None
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from pycobi import ODESystem a = ODESystem.from_yaml("model_templates.neural_mass_models.qif.qif", auto_dir="", init_cont=False)
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from .decide_split import * from .visualize_split import * from .tuning import * from .reversal_detector import * from .cosine_distance_clustering import *
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import sys try: from data_loader import * except: sys.path.append('/workspace/Walk_Video_PyTorch/project/dataloader') from data_loader import *
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from celery import Celery app = Celery('tasks', broker='amqp://') @app.task() def add(x, y): return x + y if __name__ == '__main__': app.start()
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graphlan_annotate.py --annot Annotation_File_RCLR_and_Colour_updated_V4.txt Data.txt plotfile.xml graphlan.py plotfile.xml Figure_3_V4.png --dpi 300 --size 15
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"""Metrics sub-module.""" from .metric import Metric # Link in report function to list available metrics from specparam.reports.options import check_metrics
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from scene_graph_prediction.pointnet2_dir.pointnet2 import data, models, utils from scene_graph_prediction.pointnet2_dir.pointnet2._version import __version__
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""" BdPy preprocessing package This package is a part of BdPy """ from .interface import * from .select_top import * from .preprocessor import Preprocessor
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from prodict import Prodict cols = Prodict( SLD=["TIME", "SLD"], ) title_dict = Prodict(train="Training", test="Test") TIME_PAD_VALUE = -5 PAD_VALUE = 0
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from src.datasets.pvc1 import SinglePVC1Dataset, MultiPVC1Dataset from src.datasets.cadena import CadenaDataset from src.datasets.pca import PCBuilder, PCDataset
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from .af import AFLayerProbe from .base import LayerProbeBase from .linear import LinearLayerProbe from .lstm import LSTMLayerProbe from .rnn import RNNLayerProbe
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"""gui_v2 GUI helpers package. This package starts intentionally small. Legacy helpers should be copied or adapted only when they are needed by the new shell. """
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from torch import nn class Identity(nn.Module): def __init__(self): super(Identity, self).__init__() def forward(self, x, *args): return x
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# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.apps import AppConfig class BiomedisaAppConfig(AppConfig): name = 'biomedisa_app'
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from abc import ABC, abstractmethod class Strategy(ABC): def __init__(self, similar_map_path): pass @abstractmethod def find_similars(self, drug_id): pass
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#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. from slowfast.utils.env import setup_environment setup_environment()
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import os os.environ["PYQTGRAPH_QT_LIB"] = "PyQt6" # flake8: noqa: F401 from . import geometry from . import structure from ._version import version as __version__
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from .celery import app @app.task def add(x, y): return x + y @app.task def mul(x, y): return x * y @app.task def xsum(numbers): return sum(numbers)
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from .version import __version__ from .nv_input import input_parser, gzip_check from .nv_valid import fastx_valid, bed_valid from .nv_align import check_exe, align_mm
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from .annotate import annotate_interactive from .kmeans import kmeans from .leiden import leiden __all__ = [ "annotate_interactive", "kmeans", "leiden", ]
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from pathlib import Path from typing import TypeAlias PathLike: TypeAlias = str | Path """A file-system path accepted as either a plain string or a `pathlib.Path`."""
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#from acdc_nn.cmd import main #from acdc_nn import nn from acdc_nn import nn from acdc_nn import util from acdc_nn.acdc_nn import ACDC3D, ACDCSeq, load_prot, run_tests
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from setuptools import setup from Cython.Build import cythonize setup( ext_modules=cythonize('cyutils.pyx') ) # Compile using python setup.py build_ext --inplace
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""" Runs the web server """ from os import sched_getaffinity from . import application application.run(host='0.0.0.0', port=8080, workers=len(sched_getaffinity(0)))
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from flask import Flask def create_app(): return Flask("app") def create_app2(foo, bar): return Flask("_".join(["app2", foo, bar])) def no_app(): pass
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import numpy as np import os def signal_preprocess(signal): signal = np.array(signal) signal = (signal - np.average(signal)) / np.var(signal) return signal
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from setuptools import setup setup( name='DTox', author='Yun Hao', version='0.0.1', description='Deep learning for Toxicology', packages=['DTox'] )
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from typing import Tuple, Union import numpy from angorapy.common.senses import Sensation StepTuple = Tuple[Sensation, Union[float, numpy.ndarray], bool, bool, dict]
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import numpy as np from . import baselines from . import renishaw as rshaw from . import wavelets from . import images #test_data = np.loadtxt('../test-spectrum.txt')
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# -*- coding: utf-8 -*- # Copyright (c) 2016-2018 Braintech Sp. z o.o. [Ltd.] <http://www.braintech.pl> # All rights reserved. """Package providing Wii Read Manager."""
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from .mua_spike_extractor import (extract_mua_spikes) from .spike_features import (return_mfr, return_responding_channels) from .networkevents import (return_mua_network)
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# -*- coding: utf-8 -*- # @Author : Yue Liu # @Email : yueliu19990731@163.com # @Time : 2022/9/21 20:58 from .random_seed import setup_seed __all__ = ['setup_seed']
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__author__ = 'quentin' import unittest class TestExple(unittest.TestCase): def test_exple(self): ans = 1 ref = 1 self.assertEqual(ans, ref)
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# pylint: disable=wildcard-import """ Customized data loader for video classification related tasks. """ from __future__ import absolute_import from .classification import *
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# This will make sure the app is always imported when # Django starts so that shared_task will use this app. from .celery import app as celery_app __all__ = ('celery_app',)
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"""Algorithms sub-module.""" from .algorithm import Algorithm # Link in report function to list available algorithms from specparam.reports.options import check_algorithms
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from biss.dummy import dummy_world def test_dummy() -> None: # given expected = "dummy" # when dummy = dummy_world() # then assert expected == dummy
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from .metadata import Metadata from .loader import LoadChannels from .loader import LoadMUASpikes from .mua_spikes import MUASpikeManager from .ca_spikes import CaSpikeManager
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parameterNames = { 'QCDscale_Higgs_ggH': 'QCD scale ggF inclusive', 'QCDscale_Higgs_ggH2in': 'QCD scale ggF #geq 2 jets', 'ATLAS_LUMI_2012': 'luminosity', 'sys':'sys', }
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# -*- coding: utf-8 -*- # @Author: gviejo # @Date: 2022-01-17 17:22:19 # @Last Modified by: gviejo # @Last Modified time: 2022-01-17 17:22:55 from .eeg_processing import *
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import sys import os path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../densratio/')) sys.path.insert(0, path) from densratio import densratio import helpers
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# pylint: disable=wildcard-import """Object Tracking, Visual Tracker Benchmark http://www.visual-tracking.net""" from __future__ import absolute_import from .tracking import *
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"""Top-level package for stringdb. Imports the api module""" from .api import * __author__ = """Peter C DeWeirdt""" __email__ = 'petedeweirdt@gmail.com' __version__ = '0.1.5'
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# -*- coding: utf-8 -*- # @Author: gviejo # @Date: 2022-01-17 13:50:29 # @Last Modified by: gviejo # @Last Modified time: 2022-01-17 13:50:48 from .neural_ensemble import *
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# pylint: disable=wildcard-import """Single-shot Object Detection.""" from __future__ import absolute_import from .ssd import * from .presets import * from .vgg_atrous import *
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import abc from carbonserver.api import schemas class Runs(abc.ABC): @abc.abstractmethod def add_run(self, run: schemas.RunCreate): raise NotImplementedError
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# -*- coding: utf-8 -*- # Copyright (c) 2016-2018 Braintech Sp. z o.o. [Ltd.] <http://www.braintech.pl> # All rights reserved. """Package managing tag and proving tag proxy."""
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from .monomers import Monomer from .polyestimator import PolyEstimator from .polymaker import PolyMaker from . import _version __version__ = _version.get_versions()["version"]
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""" Boilerplate dev test """ from imports import * if __name__ == "__main__": abf = pyabf.ABF(PATH_DATA+"/180415_aaron_temp.abf") print(abf.sweepY) print(abf.sweepC)
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from ._activations import get_activators_n_seqlets, get_activators_max_seqlets, get_pfms from ._plot import plot_filter_logo, plot_filter_logos from ._annotate import annotate_pfms
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from .caper_client import CaperClient, CaperClientSubmit from .caper_runner import CaperRunner __all__ = ['CaperClient', 'CaperClientSubmit', 'CaperRunner'] __version__ = '2.3.2'
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""" Customized data loader for video classification related tasks. """ from __future__ import absolute_import from .dataset_classification import * from .multigrid_helper import *
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from .voxel_mapping import innervation_location, innervation_image, coordinates_to_image from .flatmap_wrapper import GeneralMap, Flatmap, BarycentricMap from .barycentric import *
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from . import data_augmentation from . import utils from . import training from . import models from . import dataset from . import pycv_link from . import committor_regularisation
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""" Utilities for converting graphs into sets of tensors for machine learning models """ from .crystal_preprocessor import * from .mol_preprocessor import * from .tfrecord import *
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import os # please refer to the readme on where to get the parameters. Save them in this folder: folder_with_parameter_files = os.path.join(os.path.expanduser('~'), 'hd-bet_params')
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from mongoengine import * class LicenseModel(DynamicDocument): id = SequenceField(primary_key=True) name = StringField() url = StringField() __all__ = ["LicenseModel"]
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from ._gpu_tree import GPUTreeExplainer try: from ._version import version as __version__ except ImportError: __version__ = "0.0.0-not-built" __all__ = ["GPUTreeExplainer"]
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import os import sys extensions = ['sphinx.ext.autodoc', 'celery.contrib.sphinx'] autodoc_default_flags = ['members'] sys.path.insert(0, os.path.abspath(os.path.dirname(__file__)))
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from .mua_plots import (rasterplot, opto_rasterplot, rasterplot_inlet, rasterplot_drugs) from .figure_plots import (plot_pairs, plot_pairs_drugapp) from .ca_plots import (plot_catrace)
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class ConfigurationError(Exception): pass class PreprocessingError(Exception): pass class PlottingError(Exception): pass class SegmentationError(Exception): pass
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# -*- coding: utf-8 -*- # Copyright (c) 2016-2018 Braintech Sp. z o.o. [Ltd.] <http://www.braintech.pl> # All rights reserved. """Package providing proxy for signal data and info."""
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import os # please refer to the readme on where to get the parameters. Save them in this folder: folder_with_parameter_files = os.path.join(os.path.expanduser('~'), 'hd_glio_params')
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from .config import nap_config from .interval_set import IntervalSet from .time_index import TsIndex from .time_series import Ts, Tsd, TsdFrame, TsdTensor from .ts_group import TsGroup
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# -*- coding: utf-8 -*- # Copyright (c) 2016-2018 Braintech Sp. z o.o. [Ltd.] <http://www.braintech.pl> # All rights reserved. """Package providing different types of ring buffers."""
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from django.urls import re_path, include from django.contrib import admin urlpatterns = [ re_path(r'^', include('biomedisa_app.urls')), re_path(r'^admin/', admin.site.urls), ]
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import os # please refer to the readme on where to get the parameters. Save them in this folder: folder_with_parameter_files = os.path.join(os.path.expanduser('~'), 'phys-seg_params')
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import torch.nn as nn from .backward_only_leaky_relu import BackwardsOnlyLeakyReLU def activation_types(): return dict(ReLU=nn.ReLU, BackwardsOnlyLeakyReLU=BackwardsOnlyLeakyReLU)
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from . import cochlear_filters from . import downsampling from . import compression from . import envelope_extraction from . import cochleagram from . import audio_transforms_cochleagram
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from .widgets import * from .selectionWindow import * from .constants import * from .methods import * from .plotWindow import * from .projectWindow import * from .terminalWindow import *
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from tfep.io.dataset.traj import TrajectoryDataset, TrajectorySubset, get_subsampled_indices from tfep.io.dataset.merged import MergedDataset from tfep.io.dataset.dict import DictDataset
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"""lightly.cli.config The lightly.cli.config module holds default configs for the command-line interface. """ # Copyright (c) 2020. Lightly AG and its affiliates. # All Rights Reserved
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# stlearn/spatial/__init__.py from . import clustering, morphology, sme, smooth, trajectory __all__ = [ "clustering", "morphology", "sme", "smooth", "trajectory", ]
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from mirp._data_import.dicom_planar_image import ImageDicomPlanarImage class ImageDicomFileDX(ImageDicomPlanarImage): def __init__(self, **kwargs): super().__init__(**kwargs)
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from mirp._data_import.dicom_planar_image import ImageDicomPlanarImage class ImageDicomFileMG(ImageDicomPlanarImage): def __init__(self, **kwargs): super().__init__(**kwargs)
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from mirp._data_import.dicom_planar_image import ImageDicomPlanarImage class ImageDicomFileCR(ImageDicomPlanarImage): def __init__(self, **kwargs): super().__init__(**kwargs)
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import random import torch import numpy as np def set_random_seed(random_seed: int = 0): torch.manual_seed(random_seed) random.seed(random_seed) np.random.seed(random_seed)
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from .pseudo_spot import pseudo_spot from .sme_impute0 import sme_impute0 from .sme_normalize import sme_normalize __all__ = [ "pseudo_spot", "sme_impute0", "sme_normalize", ]
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class PreprocessingWarning(UserWarning): pass class IOWarning(UserWarning): pass class PlottingWarning(UserWarning): pass class SegmentationWarning(UserWarning): pass
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from .augmentation import Jitter, KShuffle, Random, ReverseComplement, Sequential from .tmm import TMM __all__ = ["TMM", "Jitter", "KShuffle", "Random", "ReverseComplement", "Sequential"]
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from sys import argv from msp import MSP from msp_donorlike import setup_as_adjusted_donor msp = MSP() msp.file = __file__ msp.seed = int(argv[1]) setup_as_adjusted_donor(msp) msp.run()
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"""Region Proposal Network.""" from __future__ import absolute_import from .rpn import RPN from . import bbox_clip from .anchor import RPNAnchorGenerator from .proposal import RPNProposal
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from .analysis import adj_pvals, grid, load_lrs, run, run_cci, run_lr_go from .het import get_edges __all__ = ["adj_pvals", "get_edges", "grid", "load_lrs", "run", "run_cci", "run_lr_go"]
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from utils import utils from utils.visualize import Visualize from utils import metrics from utils import heat_map __all__ = { 'utils', 'Visualize', 'metrics', 'heat_map' }
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import torch.nn as nn def cosine(source, target): source, target = source.mean(), target.mean() cos = nn.CosineSimilarity(dim=0) loss = cos(source, target) return loss.mean()
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""" The larvaworld Command Line Interface (CLI) """ from __future__ import annotations __displayname__ = "CLI" __all__: list[str] = ["SimModeParser"] from .argparser import SimModeParser
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from .ngym_random import TruncExp from .plotting import plot_env from .psychometric import plot_psychometric from .scheduler import RandomSchedule, SequentialBlockSchedule, SequentialSchedule
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"""XCP-D data files .. autofunction:: load .. automethod:: load.readable .. automethod:: load.as_path .. automethod:: load.cached """ from acres import Loader load = Loader(__package__)
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""" BdPy data format package This package is a part of BdPy """ from .pd import * from .datastore import * from .sparse import * from .features import * from .kvs import SQLite3KeyValueStore
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# pylint: disable=wildcard-import """Video action recognition, UCF101 dataset. https://www.crcv.ucf.edu/data/UCF101.php """ from __future__ import absolute_import from .classification import *
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import os import modular_splicing DATA_DIRECTORY = os.path.join( os.path.dirname( os.path.dirname(os.path.dirname(os.path.abspath(modular_splicing.__file__))) ), "data", )