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from .serialem import import_tilt_series_from_serial_em from .stub_alternative import stub from ._cli import cli
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#!/usr/bin/env python from setuptools import setup setup(use_scm_version={"write_to": "napari_imc/_version.py"})
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from ._scimath_impl import ( __all__, __doc__, sqrt, log, log2, logn, log10, power, arccos, arcsin, arctanh )
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from pandas.tests.extension.date.array import ( DateArray, DateDtype, ) __all__ = ["DateArray", "DateDtype"]
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from .generator import Generator from .discriminator import Discriminator __all__ = ['Generator', 'Discriminator']
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# from ._batch_segmentation_multiprocessing import CellposeBatchSegmentation # __all__ = ["CellposeBatchSegmentation"]
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"""Initialization module for the cali.runner package.""" from ._cali_runner import CaliRunner __all__ = ["CaliRunner"]
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import typer CLI_NAME = 'relion_tomo_view' cli = typer.Typer(name=CLI_NAME, add_completion=False, no_args_is_help=True)
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import typer CLI_NAME = 'relion_tomo_pick' cli = typer.Typer(name=CLI_NAME, add_completion=False, no_args_is_help=True)
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"""Module to hold code handling the GBK components""" from .gbk import GBK from .cds import CDS __all__ = ["GBK", "CDS"]
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from .abutting_grating_illusion import ag_distort_28, ag_distort_224, ag_distort_silhouette, save_image, get_silhouette_data
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import sys class Point: def __init__(self, x, y): self.x = x self.y = y self.cost = sys.maxsize
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""" Tests for reductions where we want to test for matching behavior across Array, Index, Series, and DataFrame methods. """
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from .gemnet_oc import GemNetOC, GemNetOCLightning from .loss import L2Loss __all__ = [GemNetOC, GemNetOCLightning, L2Loss]
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from .abutting_grating_distortion import ag_distort_28, ag_distort_224, ag_distort_silhouette, save_image, get_silhouette_data
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def off_diagonal(x): n, m = x.shape assert n == m return x.flatten()[:-1].view(n - 1, n + 1)[:, 1:].flatten()
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"""Activation map computation module.""" from ._core import compute_activation_map __all__ = [ "compute_activation_map", ]
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from ._array_utils_impl import ( __all__, __doc__, byte_bounds, normalize_axis_index, normalize_axis_tuple, )
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# See: # https://web.archive.org/web/20140822061353/http://cens.ioc.ee/projects/f2py2e from numpy.f2py.f2py2e import main main()
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# from python from enum import Enum class SOURCE_TYPE(Enum): QUERY = "query" MIBIG = "mibig" REFERENCE = "reference"
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"""Subpackage containing all of pip's command line interface related code""" # This file intentionally does not import submodules
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""" This module contains functions for preprocessing neural recordings """ from yass.preprocess.run import run __all__ = ['run']
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from .particles import pick_particles from .spheres import pick_spheres from .filaments import pick_filaments from ._cli import cli
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import os import pytest @pytest.fixture(autouse=True) def set_cublas_var(): os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
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"""Autoregressive Modeling, Stats Helpers and (Re-)Sampling.""" from cedalion.math import ar_irls, ar_model, resample, stats_helpers
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from ._dists import Distribution from ._envs import Environment __all__ = ["NAME", "Distribution", "Environment"] NAME = "importlib"
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"""FOOOF - Fitting Oscillations & One-Over F""" from .version import __version__ from .fit import FOOOF from .group import FOOOFGroup
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# __all__ = ['base', 'datasets', 'model_zoo', 'utils'] # # from . import ( # base, # datasets, # model_zoo, # utils # )
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import uuid import pytest @pytest.fixture def setup_path(): """Fixture for setup path""" return f"tmp.__{uuid.uuid4()}__.h5"
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from enum import Enum class Env(str, Enum): STARCRAFT = "starcraft" MPE = "mpe" # RANDOM_SEED = 23 # ENV_NAME = "5_agents"
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from .tilt_series import GuiTiltSeries from .tilt_series_set import GuiTiltSeriesSet from .lazy_tilt_series_data import LazyTiltSeriesData
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"""Initialization code for the cali.analysis package.""" from ._extraction_runner import ExtractionRunner __all__ = ["ExtractionRunner"]
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from . import apis from . import abstraction from . import wrapper from .wrapper import batch_expectation_ps from .apis import submit_task
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from .cryoCARE import cryoCARE_train as _cryoCARE_train from .cryoCARE import cryoCARE_predict as _cryoCARE_predict from ._cli import cli
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"""Initialization code for the cali.segmentation package.""" from ._detection_runner import DetectionRunner __all__ = ["DetectionRunner"]
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#replace the definition with one containing upper and lower bounds for each bounded input feature. boundslist = [] #boundsdict[0] = (0,1)
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from .cnn import TransformerHead, MLP, ContrastiveLearningNetwork, Classifier, vit_backbone, Network, EMA from .resnet import resnet_backbone
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from pandas.core.dtypes.dtypes import SparseDtype from pandas.core.arrays.sparse import SparseArray __all__ = ["SparseArray", "SparseDtype"]
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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on 10/17/2022 at 4:42 PM Author: dzhi """ __all__ = ['dcbc', 'utilities']
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# Copyright (C) 2013 Bernd Feige # This file is part of avg_q and released under the GPL v3 (see avg_q/COPYING). """ Sleep-related modules. """
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""" .. include:: ../../README.md """ from . import register, utils from .register import register_volumes, Recipe from .utils import load_data
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from .asynchronous import asynchronous from .baseline import baseline from .isochronous import isochronous from .synchronous import synchronous
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import subprocess if __name__ == "__main__": for gpu_id in range(4): subprocess.Popen(["python", "optuna_worker.py", str(gpu_id)])
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from .dimenetplusplus import DimeNetPlusPlusLightning, DimeNetPlusPlusPotential __all__ = [DimeNetPlusPlusLightning, DimeNetPlusPlusPotential]
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import imp import os.path as osp def load(name): pathname = osp.join(osp.dirname(__file__), name) return imp.load_source('', pathname)
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# global configuration # root folder for the project project_folder_path = 'E:\\work\\q_backup_06_03_2025\\projects\\isttc_backup_23_12_2025\\'
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from .abstract_language_model import AbstractLanguageModel from .chatgpt import ChatGPT from .azuregpt import AzureGPT from .ollama import Ollama
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from pandas.tests.extension.list.array import ( ListArray, ListDtype, make_data, ) __all__ = ["ListArray", "ListDtype", "make_data"]
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from pandas.tests.extension.json.array import ( JSONArray, JSONDtype, make_data, ) __all__ = ["JSONArray", "JSONDtype", "make_data"]
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from ._impl import odeint from ._impl import odeint_adjoint from ._impl import odeint_event from ._impl import odeint_dense __version__ = "0.2.5"
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import imp import os.path as osp def load(name): pathname = osp.join(osp.dirname(__file__), name) return imp.load_source('', pathname)
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# License: GNU Affero General Public License v3 or later # A copy of GNU AGPL v3 should have been included in this software package in LICENSE.txt.
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from pandas.tests.extension.array_with_attr.array import ( FloatAttrArray, FloatAttrDtype, ) __all__ = ["FloatAttrArray", "FloatAttrDtype"]
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from .masked_mae import MaskedMeanAbsoluteError from .qhnet import QHNet, QHNetLightning __all__ = [MaskedMeanAbsoluteError, QHNet, QHNetLightning]
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# Stealing this from jupyterlab # currently main.py shouldn't really be doing much... import sys from aims_immune.aims import main sys.exit(main())
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# Copyright 2024 RADar-AZDelta # SPDX-License-Identifier: gpl3+ from riab.cli import Cli def cli(): Cli() if __name__ == "__main__": cli()
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from pydantic import BaseModel class OpenAIKeyRequest(BaseModel): api_key: str class OpenAIKeyValidationResponse(BaseModel): is_valid: bool
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from .. import ufunc _UFunc_Nin1_Nout1 = ufunc _UFunc_Nin2_Nout1 = ufunc _UFunc_Nin1_Nout2 = ufunc _UFunc_Nin2_Nout2 = ufunc _GUFunc_Nin2_Nout1 = ufunc
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# Error Messages METAFILE_NOTFOUND_ERR = "Metadata file {} could not be parsed! Exception: {}!" LOAD_FAIL_MSG = "Failed to load image: {}\nException: {}"
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__version__ = '1.0.0' from MolMM.get_models import * from MolMM.dataset_multi import * from MolMM.modules_multi import * from MolMM.models_multi import *
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"""Some default parameter values for all simulations and inference and candy.""" max_sigma_c = 1 min_sigma_c = 0.1 max_p_scaling = 2 min_p_scaling = 0.1
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from dataclasses import dataclass, field from typing import Optional, Dict @dataclass class VideoFilesConfig: target_directory: Optional[str] = None
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# -*- coding: utf-8 -*- # @Author : PistonYang(pistonyang@gmail.com) from .transforms import * from .autoaugment import * from .dynamic_transform import *
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from app.CaFire import App def main(): """ Application entry point """ app = App() app.mainloop() if __name__ == "__main__": main()
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from utils import Option def getHyper_svm(): hyperDict = { "C" : 1.0 } return Option(hyperDict)
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import numpy.exceptions as ex ex.AxisError("test") ex.AxisError(1, ndim=2) ex.AxisError(1, ndim=2, msg_prefix="error") ex.AxisError(1, ndim=2, msg_prefix=None)
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"""Contains newick tree generation functionality""" from .newick_tree import generate_newick_tree, save_trees __all__ = ["generate_newick_tree", "save_trees"]
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from .imaxt import ImaxtFileReader from .mcd import McdFileReader from .txt import TxtFileReader __all__ = ["ImaxtFileReader", "McdFileReader", "TxtFileReader"]
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import pytest @pytest.fixture(params=[True, False]) def sort(request): """Boolean sort keyword for concat and DataFrame.append.""" return request.param
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"""Contains classes and methods to create and manipulate networks as used in BiG-SCAPE, as well as methods to execute affinity propagation on arbitrary graphs """
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# file generated by setuptools_scm # don't change, don't track in version control __version__ = version = '2.9.0.post0' __version_tuple__ = version_tuple = (2, 9, 0)
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from typing import NamedTuple class Region(NamedTuple): """Defines a rectangular region of the screen.""" x: int y: int width: int height: int
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from smac.examples.rllib.env import RLlibStarCraft2Env from smac.examples.rllib.model import MaskedActionsModel __all__ = ["RLlibStarCraft2Env", "MaskedActionsModel"]
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"""Initialization for cali_logger.""" from ._logger import LOGGER as cali_logger from ._logger import set_console_level __all__ = ["cali_logger", "set_console_level"]
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# -*- coding: utf-8 -*- import os import sys sys.path.append(os.path.dirname(os.path.abspath(__file__))) def main(): pass if __name__ == "__main__": main()
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__all__ = ("dumps", "dump") __version__ = "1.2.0" # DO NOT EDIT THIS LINE MANUALLY. LET bump2version UTILITY DO IT from pip._vendor.tomli_w._writer import dump, dumps
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class empty: def __init__(self): pass #self.recordings = lambda:None #self.resources = lambda:None class attribs: pass
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from typing import Any import numpy as np import numpy.ma m : np.ma.MaskedArray[Any, np.dtype[np.float64]] = np.ma.masked_array([1.5, 2, 3], mask=[True, False, True])
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""" Useful utilities for quantum chemistry related task """ from .conversions import get_ps # pylint:disable=unused-import # backward compatibility for the entry point
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from yass.neuralnetwork.model_detector import Detect, Detect_Single from yass.neuralnetwork.model_denoiser import Denoise __all__ = ['Detect', 'Detect_Single', 'Denoise']
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from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl import flags FLAGS = flags.FLAGS FLAGS(["main.py"])
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import numpy as np a = np.empty((2, 2)).flat a.base a.copy() a.coords a.index iter(a) next(a) a[0] a[[0, 1, 2]] a[...] a[:] a.__array__() a.__array__(np.dtype(np.float64))
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from dp5.nmr_processing.nmr_ai import NMRData from dp5.nmr_processing.proton.process import proton_processing from dp5.nmr_processing.carbon.process import carbon_processing
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from .logger import initialize_logger, logger_factory from .lr_scheduler import LRScheduler, lr_scheduler_factory from .optimizer import optimizer_factory, optimizers_factory
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from molmap.model import cbks, loss, net, importance from .model import RegressionEstimator, MultiClassEstimator, MultiLabelEstimator from .model import save_model, load_model
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__all__ = ['convnet', 'resnet', 'base_module', 'glsnn', 'qsnn', 'resnet19_snn'] from . import ( convnet, resnet, base_module, glsnn, qsnn, resnet19_snn )
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Python
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import numpy as np import stf import stfio def tdms_open(fn): record = stfio.read_tdms(fn) if record is None: return None return record['data'], record['dt']
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Python
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"""Gaussian-copula mutual information estimators.""" from ._core import * # noqa: F401,F403 from ._core import __all__ as __all__ from ._version import __version__ as __version__
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Python
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from dp5.conformer_search import * from dp5.conformer_search.conf_prune import StrictRMSDPrune as conf_prune from dp5.conformer_search.base_cs_method import BaseConfSearch, ConfData
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Python
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4
from asap.dataloader.base import BaseDataset from asap.dataloader.wg import WGDataset from asap.dataloader.bounded import BoundedDataset from asap.dataloader.peak import PeakDataset
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Python
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"""CINEMA-OT - Causal Independent Effect Module Attribution + Optimal Transport, for single-cell level treatment effect identification""" __version__ = "0.0.3" from . import cinemaot
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Python
182
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__version__ = "0.7.1" from .model import EfficientNet, VALID_MODELS from .utils import ( GlobalParams, BlockArgs, BlockDecoder, efficientnet, get_model_params, )
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Python
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"""Driver extensions for VMC""" # Don't import modules automatically to avoid execution # Users should import explicitly if needed __all__ = [ 'vmc_ex', 'expect_grad_ex', ]
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Python
184
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from . import detector, record, tasks, trigger, utils, viz from ._version import __version__ from .utils.config import sys_info from .utils.logs import add_file_handler, set_log_level
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Python
185
2
from cedalion.sigdecomp.multimodal.cca import ElasticNetCCA, StructuredSparseCCA from cedalion.sigdecomp.multimodal.tcca import ElasticNetTCCA, StructuredSparseTCCA, temporal_embedding
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Python
186
8
from molmap.feature.sequence.nas.global_feature.nacutil import make_kmer_list if __name__ == "__main__": for e in make_kmer_list("AAA", "ACGT"): print(e) pass
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Python
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class StringConverter(dict): def __contains__(self, item): return True def __getitem__(self, item): return str def get(self, default=None): return str
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Python
190
8
# Copyright (C) 2008-2010,2013 Bernd Feige # This file is part of avg_q and released under the GPL v3 (see avg_q/COPYING). """ ECG-related modules. """ __all__=['ECG'] from .ECG import ECG
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Python
190
8
# Copyright (C) 2008-2010,2013 Bernd Feige # This file is part of avg_q and released under the GPL v3 (see avg_q/COPYING). """ EOG-related modules. """ __all__=['EOG'] from .EOG import EOG
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Python
191
7
# params.py generated by YASS on {timestamp} dat_path = {dat_path} n_channels_dat = {n_channels_dat} dtype = {dtype} offset = {offset} sample_rate = {sample_rate} hp_filtered = {hp_filtered}