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'
