def _min_extension() -> float: from importlib import import_module try: return import_module( "cleaning._core").ExtentRule().minextent # type: ignore except ImportError: from utils.logconfig import getLogger getLogger(__name__).warning( "Could not obtain min extension from the cleaning module") return .25
# -*- coding: utf-8 -*- """ Allows creating modals from anywhere """ from copy import deepcopy, copy from functools import partial from typing import Dict, List, Tuple, Any, Union, Sequence, cast from bokeh.document import Document from bokeh.models import Widget, Button import numpy as np from view.fonticon import FontIcon from utils import initdefaults, dataclass, dflt from utils.logconfig import getLogger from taskmodel.base import Rescaler from modaldialog import dialog LOGS = getLogger(__name__) TITLE = "# " SUBTITLE = "## " PARAGRAPH = "### " BOLD = "* " EMPHASIS = "** " class AdvancedTab: "a tab in the widget" __inds = 0 def __init__(self, title: str, *items: Tuple[str, ...], body: str = "") -> None:
from tasksequences import splitoligos from utils import NoArgs from utils.logconfig import getLogger from .._peakinfo import PeakInfoModelAccess from ._processors import runbead from ._jobs import JobRunner from ._plotmodel import PeaksPlotModel from ._taskaccess import (EventDetectionTaskAccess, PeakSelectorTaskAccess, SingleStrandTaskAccess, BaselinePeakFilterTaskAccess, FitToReferenceAccess, FitToHairpinAccess) # pylint: disable=unused-import,wrong-import-order,ungrouped-imports from peakfinding.processor.__config__ import PeakSelectorTask LOGS = getLogger(__name__.replace('_', '')) # pylint: disable=too-many-instance-attributes class PeaksPlotModelAccess(SequencePlotModelAccess, DataCleaningModelAccess): "Access to peaks" def __init__(self): DataCleaningModelAccess.__init__(self) SequencePlotModelAccess.__init__(self) self.eventdetection = EventDetectionTaskAccess(self) self.peakselection = PeakSelectorTaskAccess(self) self.singlestrand = SingleStrandTaskAccess(self) self.baselinefilter = BaselinePeakFilterTaskAccess(self) self.fittoreference = FitToReferenceAccess(self)
from typing import (Dict, Sequence, NamedTuple, List, Type, Tuple, Union, Optional, Iterable, Pattern, cast) import numpy as np from peakfinding.peaksarray import Output as PeakFindingOutput, PeakListArray from peakfinding.processor import PeaksDict, SingleStrandTask, BaselinePeakTask from sequences import splitoligos, read as _read from taskmodel import Task, Level from utils.logconfig import getLogger from utils import (StreamUnion, initdefaults, updatecopy, DefaultValue) from ...tohairpin import (HairpinFitter, PeakGridFit, Distance, PeakMatching, PEAKS_TYPE) from ..._base import Range LOGS = getLogger("__name__") class DistanceConstraint(NamedTuple): hairpin: Optional[str] constraints: Dict[str, Range] def rescale(self, value: float) -> 'DistanceConstraint': "rescale factors (from µm to V for example) for a given bead" return type(self)( self.hairpin, {i: j.rescale(i, value) for i, j in self.constraints.items()}) Fitters = Dict[Optional[str], HairpinFitter]
import os import pandas as pd from sklearn.utils import shuffle from utils.logconfig import getLogger """ 存放image和label路径地址的list note:需要使用绝对路劲,防止出现 NoneTpye Object 的 Error 使用logger格式化训练日志 """ label_list = [] image_list = [] image_dir = 'D:\\data\\LaneSeg\\Image_Data' label_dir = 'D:\\data\\LaneSeg\\Gray_Label' logger = getLogger() """ 目录结构: Image_Data/ road02/ Record002/ Camera 5/ ... Camera 6 Record003 .... road03 road04 Gray_Label/ Label_road02/ Label Record002/ Camera 5/
from taskcontrol.modelaccess import TaskAccess from taskmodel import RootTask, DataSelectionTask from utils.logconfig import getLogger from utils import updatecopy, NoArgs from ...reporting.batch import readconstraints from ._processors import runrefbead # pylint: disable=unused-import,wrong-import-order,ungrouped-imports from eventdetection.processor.__config__ import EventDetectionTask from peakfinding.processor.__config__ import (PeakSelectorTask, SingleStrandTask, BaselinePeakFilterTask) from peakcalling.processor.__config__ import FitToHairpinTask, FitToReferenceTask LOGS = getLogger(__name__.replace("_", "")) _DUMMY = type( '_DummyDict', (), dict(get=lambda *_: None, __contains__=lambda _: False, __len__=lambda _: 0, __iter__=lambda _: iter(())))() ConstraintsDict = Dict[RootTask, Constraints] class FitToReferenceConfig: """ stuff needed to display the FitToReferenceTask """ def __init__(self): self.name: str = 'hybridstat.fittoreference'