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from .basic_model import BasicModel from .basic_unet import UNet from .nn_unet import nnUNet from .swin_unetr import SwinUNETR
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# -*- coding: utf-8 -*- """Implementations of feature tracking methods.""" from pysteps.tracking.interface import get_method
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Python
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# -*- coding: utf-8 -*- """Implementations of feature detection methods.""" from pysteps.feature.interface import get_method
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from .Alignment import Alignment from .FASTQ_File import FASTQ_File from .Library import Library from .Template import Template
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class TestAlphanumeric: def test_test_valid(self): pass def test_validate_alphanumeric_text(self): pass
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from ._measurements import Measurements from ._metadata_group import MetadataGroup from ._relationship_key import RelationshipKey
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import torch.nn as nn class BaseAggregator(nn.Module): def __init__(self): pass def forward(self): pass
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Python
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from typing import Tuple import numpy as np from nam.config.base import Config Config = Config DataType = Tuple[np.array, ...]
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from typing import List, Callable, Union, Any, TypeVar, Tuple # from torch import tensor as Tensor Tensor = TypeVar('torch.tensor')
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class TestIdentify: def test_get_object_categories(self): pass def test_get_object_measurements(self): pass
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import dgl.backend as F import torch def zerocopy_from_numpy(x): return torch.from_numpy(x) # return F.zerocopy_from_numpy(x)
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import pyControl.framework as fw import pyControl.hardware as hw import pyControl.state_machine as sm from pyControl.utility import *
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from .eddy import Eddy from .epi import EpiCorrection, ApplyTopup from .n4bias import N4BiasCorrection from .nlmeans import NonLocalMeans
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""" @author: Johanna Rahm Research group Heilemann Institute for Physical and Theoretical Chemistry, Goethe University Frankfurt a.M. """
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Python
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from .DataGenerator import DataGenerator from .GridTools import GridTools from .NormalizeData import MinMaxParam, NormalizeData, NormParam
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Python
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from knn.knn import ( build_index, display_results, display_results_seq, search_results, search_results_2, search )
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class Finished: def __init__(self, measurements, cancelled): self.measurements = measurements self.cancelled = cancelled
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from ._hub import * import warnings warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", FutureWarning)
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from .plots import setup_recordAPs, setup_recordTraces from .save import SaveResults from .utils import * from .wrapper import AnalysesWrapper
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from ._factory import * import warnings warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", FutureWarning)
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from ._registry import * import warnings warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", FutureWarning)
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from ._features import * import warnings warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", FutureWarning)
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import os import sys import pytest args = sys.argv[1:] if "NO_RHINO" in os.environ: args += ["-m", "not rhino"] sys.exit(pytest.main(args))
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from ._post_run_exception import PostRunException from ._prepare_run_exception import PrepareRunException from ._run_exception import RunException
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__author__ = ['Vikram Singh', 'Vikram Singh'] __email__ = ['vikram.singh7571@gmail.com', 'vikramsingh@cuhimachal.ac.in'] __version__ = '0.1.0-dev0'
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from .perlin3d import generate_fractal_noise_3d, generate_perlin_noise_3d from .perlin2d import generate_perlin_noise_2d, generate_fractal_noise_2d
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from ._features_fx import * import warnings warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", FutureWarning)
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#!/usr/bin/env python3 import os submit_files = os.listdir("submit_files") for f in submit_files: os.system(f"condor_submit submit_files/{f}")
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# deep learning from .modelGenerator import NetworkGenerator from .DataSet import DataSet from .NeuralNet import NeuralNet from .metaqnn import MetaQNN
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import numpy as np def scale_z(params, mean, std): return (params - mean) / std def unscale_z(params, mean, std): return params * std + mean
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# -*- coding: utf-8 -*- """Implementations of deterministic and ensemble downscaling methods.""" from pysteps.downscaling.interface import get_method
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from ..functions.image_processing import median_filter def medianfilter(image, window_size, mode): return median_filter(image, window_size, mode)
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from ._module import Module from ._identify import Identify from ._image_processing import ImageProcessing from ._plugin_importer import PluginImporter
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from ._finished import Finished from ._paused import Paused from ._progress import Progress from ._resumed import Resumed from ._started import Started
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from ._float_range import FloatRange from ._range import Range from .integer_range import IntegerRange from .integer_range import IntegerOrUnboundedRange
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class TestBinaryMatrix: def test_to_value(self): pass def test_get_matrix(self): pass def test_get_size(self): pass
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class TestImagePredicate: def test_is_stack(self): pass def test_fn_filter(self): pass def test_test_valid(self): pass
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from ._image_list_subscriber import ImageListSubscriber from ._label_list_subscriber import LabelListSubscriber from ._list_subscriber import ListSubscriber
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import os import sys curr_dir = os.path.basename(os.path.abspath(os.curdir)) if curr_dir == 'configs' and '..' not in sys.path: sys.path.insert(0, '..')
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import os import sys curr_dir = os.path.basename(os.path.abspath(os.curdir)) if curr_dir == 'hpsearch' and '..' not in sys.path: sys.path.insert(0, '..')
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class TestDoSomething: def test_get_label(self): pass def test_set_label(self): pass def test_on_event_fired(self): pass
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import copy import torch.nn as nn def clones(module, N): "Produce N identical layers." return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
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import torch def to_torch_batch(batch): batch = torch.stack([batch.x[batch.batch == n].squeeze() for n in range(batch.num_graphs)],axis=0) return batch
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from cellprofiler_core.utilities.zmq.communicable.reply.upstream_exit._upstream_exit import ( UpstreamExit, ) class BoundaryExited(UpstreamExit): pass
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import pyControl.hardware as _h class LED_driver(_h.Digital_output): def __init__(self, port): super().__init__(port.DIO_A, pulse_enabled=True)
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from ...utilities.zmq.communicable.reply import Reply class Ack(Reply): def __init__(self, message="THANKS"): Reply.__init__(self, message=message)
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class TestVanillaImage: def test_get_name(self): pass def test_provide_image(self): pass def test_release_memory(self): pass
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class TestAbstractImage: def test_provide_image(self): pass def test_get_name(self): pass def test_release_memory(self): pass
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__version__ = "3.0.0" from .sc3D import SpatialOmicArray from .transformations import transformations __all__ = [ "SpatialOmicArray", "transformations", ]
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class TestFilterPredicate: def test_test_valid(self): pass def test_encode_symbol(self): pass def test_decode_symbol(self): pass
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from model.egnn.egnn_pytorch_geometric import EGNN_Sparse, EGNN_Sparse_Network from model.egnn.egnn_pytorch import EGNN_Network from model.egnn.utils import Classifier
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class TestSettingsGroup: def test_append(self): pass def test_visible_settings(self): pass def test_pipeline_settings(self): pass
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class TestListSubscriber: def test_get_value_text(self): pass def test_set_value_text(self): pass def test_test_valid(self): pass
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# this is automatically updated after reproschema new release CONTEXTFILE_URL = "https://raw.githubusercontent.com/ReproNim/reproschema/main/releases/1.0.0/reproschema"
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class TestMetadataPredicate: def test_set_metadata_keys(self): pass def test_do_filter(self): pass def test_test_valid(self): pass
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# -*- coding: utf-8 -*- """ @author: Jussi (jnu@iki.fi) """ from setuptools import setup, find_packages setup( name='megsimutils', packages=find_packages(), )
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from ...utilities.zmq.communicable.reply import Reply class OmeroLogin(Reply): def __init__(self, credentials): Reply.__init__(self, credentials=credentials)
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from ...utilities.zmq.communicable.reply import Reply class DebugCancel(Reply): """If sent in response to DebugWaiting, the user has changed his/her mind""" pass
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class TestDataTypes: def test_get_data_types(self): pass def test_decode_data_types(self): pass def test_encode_data_types(self): pass
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from torchmeta.utils import data from torchmeta.utils.metrics import hardness_metric from torchmeta.utils.prototype import get_num_samples, get_prototypes, prototypical_loss
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_all__ = ["utils"] from .utils import Rd_sampling, ScatteredHypersphere, sparsity_to_x_intercept, get_mean_and_ci from .figure_utils import * from .network_diagram import *
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from .config_parsing import parse_opacus_params from .model_handling import opacus_model_fix, prep_for_opacus_training from .training_utils import handle_dynamic_batch_size
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DEBUG=True VESSEL_DIR = '/Volumes/GAITDRIVE/lung/vessel12' SVESSEL_DIR = '/Users/ari/th/VesselSynth' ENDOSTROKE_DIR = './data/gendostroke' ISLES2018_DIR = './data/gisles2018'
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from .basic_models import StateSpaceModel, OscillatorModel, AutoRegModel, GeneralSSModel # noqa: F401 from importlib.metadata import version __version__ = version('somata')
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from cellprofiler_library.functions.image_processing import gaussian_filter def gaussianfilter(image, sigma): return gaussian_filter( image, sigma, )
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#!/usr/bin/env python3 # -*- coding: UTF-8 -*- # check GaNDLF wiki for versioning and release guidelines: https://github.com/mlcommons/GaNDLF/wiki __version__ = "0.1.6-dev"
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import numpy as np def rmse(y_true, y): return np.sqrt(np.mean((y_true - y)**2)) def explained_variance(y_true, y): return 1 - np.var(y_true - y) / np.var(y_true)
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from .model import ( Activity, Item, Protocol, Response, ResponseActivity, ResponseOption, ) from .utils import identify_model_class, write_obj_jsonld
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from .pac_model import ( # user-facing functions to analyze phase-amplitude coupling # noqa: F401, E501 fit_pac_regression, kmod, phimod, mvar_ssm, optimize_arp)
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"""Martensite substructure generation: parent grains -> packets -> blocks -> orientations. See dragen/substructure/run.py for the pipeline and the order the stages run in. """
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# -*- coding: utf-8 -*- """Top-level package for visual_coding_2p_analysis.""" __author__ = """Nicholas Cain""" __email__ = 'nicholasc@alleninstitute.org' __version__ = '0.1.0'
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name = "sspslam" version_info = (0, 2, 0) # (major, minor, patch) dev = False v = ".".join(str(v) for v in version_info) dev_v = ".dev0" if dev else "" version = f"{v}{dev_v}"
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from enum import Enum class BackgroundExclusionMode(str, Enum): """Background exclusion method""" THRESHOLD = "Threshold" MASK = "Mask" OBJECTS = "Within Objects"
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# -*- coding: utf-8 -*- """ Methods for constructing bandpass filters and decomposing 2d precipitation fields into different spatial scales. """ from .interface import get_method
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# -*- coding: utf-8 -*- """ .. include:: ../README.md """ __author__ = """Vito Zanotelli, Anton Rau""" __email__ = "vito.zanotelli@uzh.ch, anton.rau@uzh.ch" __version__ = "2.1.8"
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from ..functions.image_processing import morphology_opening def opening(image, structuring_element): return morphology_opening( image, structuring_element, )
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class TestBeforeChangeSettingEvent: def test_cancel_change(self): pass def test_allow_change(self): pass def test_get_cancel_reason(self): pass
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# -*- coding: utf-8 -*- """Methods for post-processing of forecasts.""" from . import ensemblestats from .diagnostics import * from .interface import * from .ensemblestats import *
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#!/usr/bin/python3 __all__ = [ 'association', 'binnet', 'coex', 'de', 'gocovt', 'lcpm', 'norm', 'normalisr', 'parallel', 'qc', 'run'] from . import * assert __name__ != "__main__"
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__version__ = '1.2.2' from . util import * from . datasetgenemap import * from . DNN import * from . util_Mouse import * from . fit_functions import * from . data_augmentation import *
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__version__ = '1.1.2' from . util import * from . datasetgenemap import * from . DNN import * from . util_Mouse import * from . fit_functions import * from . data_augmentation import *
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from __future__ import absolute_import from __future__ import division from __future__ import print_function # pylint: disable=unused-import,wildcard-import from .deconvModel import *
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from pyunicorn import Network, mpi def master(): net = Network.Model("BarabasiAlbert", n_nodes=1000, n_links_each=10) print(net.newman_betweenness()) mpi.info() mpi.run()
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from ._after_change_setting_event import AfterChangeSettingEvent from ._before_change_setting_event import BeforeChangeSettingEvent from ._change_setting_event import ChangeSettingEvent
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from .aggregator import * from .attentionmil import * from .lamil import * from .perceiver import * from .transformer import * from .transmil import * # from model_utils import *
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""" @author: Johanna Rahm Research group Heilemann Institute for Physical and Theoretical Chemistry, Goethe University Frankfurt a.M. """ __all__ = ["addOns.py", "imageBinner", "visLocs"]
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from enum import Enum class ImageMode(str, Enum): BINARY = "Binary (black & white)" GRAYSCALE = "Grayscale" UINT16 = "uint16" COLOR = "Color" DEFAULT_COLORMAP = "Default"
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class TestSubdirectoryFilter: def test_get_value_string(self): pass def test_alter_for_create_batch_files(self): pass def test_test_valid(self): pass
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""" Author: Mingjian He <mh1@stanford.edu> utils module contains helper functions used throughout SOMATA """ from .helper_functions import consecutive, estimate_r, wrapToPi # noqa: F401
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from unittest import TestCase from .files import upload_training, get_npy_files class LoggerTest(TestCase): def test_print_debug(self): upload_training('UNet',1,0.001,'MNIST')
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Python
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import os def savefigs(fig_name, fig_dir, fig): if not os.path.exists(fig_dir): os.makedirs(fig_dir) fig.savefig(os.path.join(fig_dir,str(fig_name) + '.png'), format='png')
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Python
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from ._dependency import Dependency from ._image_dependency import ImageDependency from ._measurement_dependency import MeasurementDependency from ._object_dependency import ObjectDependency
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Python
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import numpy as np from pyunicorn.timeseries import RecurrenceNetwork x = np.sin(np.linspace(0, 10 * np.pi, 1000)) net = RecurrenceNetwork(x, recurrence_rate=0.05) print(net.transitivity())
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Python
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""" Author: Mingjian He <mh1@stanford.edu> multitaper module contains multitaper spectrogram computation used in SOMATA """ from .fast_psd_multitaper import fast_psd_multitaper # noqa: F401
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from ._color_image import ColorImage from ._mask_image import MaskImage from ._monochrome_image import MonochromeImage from ._objects_image import ObjectsImage from ._url_image import URLImage
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class TestDoThings: def test_count(self): pass def test_get_label(self): pass def test_set_label(self): pass def test_on_event_fired(self): pass
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from ._event import Event class URLsCleared(Event): def __init__(self): super(self.__class__, self).__init__() def event_type(self): return "File list has been cleared"
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Python
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from .logger import print_debug from .savefigs import savefigs from .timer import Timer from .files import get_npy_files, upload_training __all__ = ['print_debug','savefigs','Timer','get_npy_files']
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""" @author: Johanna Rahm Research group Heilemann Institute for Physical and Theoretical Chemistry, Goethe University Frankfurt a.M. """ __all__ = ["saveAddOns", "saveImageBinner", "saveVisLocs"]
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Python
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# -*- coding: utf-8 -*- """ Methods for generating stochastic perturbations of 2d precipitation and velocity fields. """ from .interface import get_method from . import utils, motion, fftgenerators