sas.qtgui.Perspectives.SizeDistribution package

Subpackages

Submodules

sas.qtgui.Perspectives.SizeDistribution.SizeDistributionLogic module

class sas.qtgui.Perspectives.SizeDistribution.SizeDistributionLogic.SizeDistributionLogic(data=None)

Bases: object

All the data-related logic. This class deals exclusively with Data1D/2D No QStandardModelIndex here.

__dict__ = mappingproxy({'__module__': 'sas.qtgui.Perspectives.SizeDistribution.SizeDistributionLogic', '__doc__': '\n    All the data-related logic. This class deals exclusively with Data1D/2D\n    No QStandardModelIndex here.\n    ', '__init__': <function SizeDistributionLogic.__init__>, 'data': <property object>, 'isLoadedData': <function SizeDistributionLogic.isLoadedData>, 'setDataProperties': <function SizeDistributionLogic.setDataProperties>, 'computeDataRange': <function SizeDistributionLogic.computeDataRange>, 'computeBackground': <function SizeDistributionLogic.computeBackground>, 'computeTrustRange': <function SizeDistributionLogic.computeTrustRange>, 'fitBackground': <function SizeDistributionLogic.fitBackground>, 'newDataPlot': <function SizeDistributionLogic.newDataPlot>, 'newSizeDistrPlot': <function SizeDistributionLogic.newSizeDistrPlot>, '__dict__': <attribute '__dict__' of 'SizeDistributionLogic' objects>, '__weakref__': <attribute '__weakref__' of 'SizeDistributionLogic' objects>, '__annotations__': {}})
__doc__ = '\n    All the data-related logic. This class deals exclusively with Data1D/2D\n    No QStandardModelIndex here.\n    '
__init__(data=None)
__module__ = 'sas.qtgui.Perspectives.SizeDistribution.SizeDistributionLogic'
__weakref__

list of weak references to the object

computeBackground(constant: float, scale: float, power: float)
computeDataRange()

Compute the minimum and the maximum range of the data

computeTrustRange(qmin: float, qmax: float)

Compute the trusted range (green area in Irena)

property data
fitBackground(power: float | None, qmin: float, qmax: float) → list[float]

Estimate the background power law, scale * q^(power) :param power: if a float is given, the power is fixed; if None, the power is fitted :return: fit parameters; [scale] if power is fixed, or [scale, power] if power is fitted

isLoadedData()

accessor

newDataPlot()

Create a new 1D data instance

newSizeDistrPlot(result: MaxEntResult, qmin: float, qmax: float)

Create a new 1D data instance based on fitting results

setDataProperties()

Analyze data and set up some properties important for the Presentation layer

sas.qtgui.Perspectives.SizeDistribution.SizeDistributionPerspective module

class sas.qtgui.Perspectives.SizeDistribution.SizeDistributionPerspective.SizeDistributionWindow(parent=None)

Bases: QDialog, Ui_SizeDistribution, Perspective

The main window for the Size Distribution perspective.

__doc__ = '\n    The main window for the Size Distribution perspective.\n    '
__init__(self, /, parent: PySide6.QtWidgets.QWidget | None = None, f: PySide6.QtCore.Qt.WindowType = Default(Qt.WindowFlags), *, sizeGripEnabled: bool | None = None, modal: bool | None = None) → None

Initialize self. See help(type(self)) for accurate signature.

__module__ = 'sas.qtgui.Perspectives.SizeDistribution.SizeDistributionPerspective'
allowBatch()

Can this perspective handle batch processing, default no

allowSwap()

Tell the caller we don’t accept swapping data

clearStatistics()

Clear the output box

closeEvent(event)

Overwrite QDialog close method to allow for custom widget close

communicator()
data_plot_signal
enableButtons()

Enable buttons when data is present, else disable them

eventFilter(widget: QObject, event: QEvent) → bool

Catch enter key presses and update data plot

ext = 'ps'
fitComplete(result: MaxEntResult) → None

Receive and display fitting results “result” is a tuple of actual result list and the fit time in seconds

fittingCompleted(result: MaxEntResult | None) → None

Send the finish message from calculate threads to main thread

fittingError(etype, value, traceback)

Handle error in the calculation thread

fittingFinishedSignal
getBackgroundParams()

Collect background parameters from the GUI state

getFlatBackgroundRange()

Collect background range from the GUI state

getMaxEntParams()

Collect Max Ent parameters from the GUI state

getPage()

serializes full state of this fit page

getPowerLawBackgroundRange()

Collect power law range from the GUI state

getState()

Collects all active params into a dictionary of {name: value} :return: {name: value}

getWeightType()

Return the weight type based on the checked radio button

help()

Open the Size Distribution help

isClosable()

Allow outsiders close this widget

isSerializable()

Tell the caller that this perspective writes its state

name = 'SizeDistribution'
onFitFlatBackground()

Fit flat background and update plot

onFitPowerLaw()

Fit background power law and update plot

onFullFit()

Perform a full fit of the size distribution

onLowQStateChanged(state: int)

Slot for state change of the subtract power law checkbox

onQuickFit()

Perform a quick fit of the size distribution

onRangeReset()

Callback for resetting qmin/qmax

plotData()

Plot data, background and background subtracted data

removeData(data_list=None)

Remove the existing data reference from the Size Distribution Perspective

resetWindow()

Reset the state of input widgets and data structures

serializeAll()

Serialize the size distribution state so data can be saved Size distribution is not batch-ready so this will only effect a single page :return: {data-id: {self.name: {inversion-state}}}

serializeCurrentPage()

Serialize and return a dictionary of {data_id: sizedistr-state} Return empty dictionary if no data :return: {data-id: {self.name: {invariant - state}}}

setClosable(value=True)

Allow outsiders close this widget

setData(data_item=None, is_batch=False)

Obtain a QStandardItem object and parse it to get Data1D/2D Pass it over to the calculator

setupMapper()
setupModel()

Update boxes with initial values

setupSlots()

Connect the use controls to their appropriate methods

setupValidators()

Apply validators to editable line edits

setupWindow()

Initialize base window state on init

staticMetaObject = PySide6.QtCore.QMetaObject("SizeDistributionWindow" inherits "QDialog": Methods:   #41 type=Signal, signature=fittingFinishedSignal(PyObject), parameters=PyObject   #42 type=Signal, signature=data_plot_signal() )
property title: str

Window title

updateBackground()

Update the background data

updateFromParameters(params)

Called by Open Project, Open Analysis, and removeData :param params: {param_name: value} -> Default values used if not valid :return: None

updateQRange(q_range_min, q_range_max)

Update the local model based on calculated values

updateStatistics(result)

Update the output box with statistics

sas.qtgui.Perspectives.SizeDistribution.SizeDistributionThread module

class sas.qtgui.Perspectives.SizeDistribution.SizeDistributionThread.SizeDistributionThread(data: Data1D, background: Data1D, params: MaxEntParameters, completefn=None, updatefn=None, yieldtime=0.01, worktime=0.01, exception_handler=None)

Bases: CalcThread

Thread performing the fit

__annotations__ = {}
__doc__ = 'Thread performing the fit'
__init__(data: Data1D, background: Data1D, params: MaxEntParameters, completefn=None, updatefn=None, yieldtime=0.01, worktime=0.01, exception_handler=None)

Initialize parameters

__module__ = 'sas.qtgui.Perspectives.SizeDistribution.SizeDistributionThread'
compute(*args, **kwargs)

Perform a work unit. The subclass will provide details of the arguments.

sas.qtgui.Perspectives.SizeDistribution.SizeDistributionUtils module

class sas.qtgui.Perspectives.SizeDistribution.SizeDistributionUtils.MaxEntParameters(qmin: float = 0.0, qmax: float = 0.1, dmin: float = 10.0, dmax: float = 1000.0, num_bins: int = 100, log_binning: bool = True, model: str = 'ellipsoid', aspect_ratio: float = 1.0, contrast: float = 1.0, sky_background: float = 1e-06, max_iterations: int = 100, use_weights: bool = True, weight_type: sas.qtgui.Perspectives.SizeDistribution.SizeDistributionUtils.WeightType = <WeightType.DI: 'dI'>, weight_factor: float = 1.0, weight_percent: float = 1.0, full_fit: bool = True)

Bases: object

__annotations__ = {'aspect_ratio': <class 'float'>, 'contrast': <class 'float'>, 'dmax': <class 'float'>, 'dmin': <class 'float'>, 'full_fit': <class 'bool'>, 'log_binning': <class 'bool'>, 'max_iterations': <class 'int'>, 'model': <class 'str'>, 'num_bins': <class 'int'>, 'qmax': <class 'float'>, 'qmin': <class 'float'>, 'sky_background': <class 'float'>, 'use_weights': <class 'bool'>, 'weight_factor': <class 'float'>, 'weight_percent': <class 'float'>, 'weight_type': <enum 'WeightType'>}
__dataclass_fields__ = {'aspect_ratio': Field(name='aspect_ratio',type=<class 'float'>,default=1.0,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'contrast': Field(name='contrast',type=<class 'float'>,default=1.0,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'dmax': Field(name='dmax',type=<class 'float'>,default=1000.0,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'dmin': Field(name='dmin',type=<class 'float'>,default=10.0,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'full_fit': Field(name='full_fit',type=<class 'bool'>,default=True,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'log_binning': Field(name='log_binning',type=<class 'bool'>,default=True,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'max_iterations': Field(name='max_iterations',type=<class 'int'>,default=100,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'model': Field(name='model',type=<class 'str'>,default='ellipsoid',default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'num_bins': Field(name='num_bins',type=<class 'int'>,default=100,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'qmax': Field(name='qmax',type=<class 'float'>,default=0.1,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'qmin': Field(name='qmin',type=<class 'float'>,default=0.0,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'sky_background': Field(name='sky_background',type=<class 'float'>,default=1e-06,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'use_weights': Field(name='use_weights',type=<class 'bool'>,default=True,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'weight_factor': Field(name='weight_factor',type=<class 'float'>,default=1.0,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'weight_percent': Field(name='weight_percent',type=<class 'float'>,default=1.0,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'weight_type': Field(name='weight_type',type=<enum 'WeightType'>,default=<WeightType.DI: 'dI'>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD)}
__dataclass_params__ = _DataclassParams(init=True,repr=True,eq=True,order=False,unsafe_hash=False,frozen=False)
__dict__ = mappingproxy({'__module__': 'sas.qtgui.Perspectives.SizeDistribution.SizeDistributionUtils', '__annotations__': {'qmin': <class 'float'>, 'qmax': <class 'float'>, 'dmin': <class 'float'>, 'dmax': <class 'float'>, 'num_bins': <class 'int'>, 'log_binning': <class 'bool'>, 'model': <class 'str'>, 'aspect_ratio': <class 'float'>, 'contrast': <class 'float'>, 'sky_background': <class 'float'>, 'max_iterations': <class 'int'>, 'use_weights': <class 'bool'>, 'weight_type': <enum 'WeightType'>, 'weight_factor': <class 'float'>, 'weight_percent': <class 'float'>, 'full_fit': <class 'bool'>}, 'qmin': 0.0, 'qmax': 0.1, 'dmin': 10.0, 'dmax': 1000.0, 'num_bins': 100, 'log_binning': True, 'model': 'ellipsoid', 'aspect_ratio': 1.0, 'contrast': 1.0, 'sky_background': 1e-06, 'max_iterations': 100, 'use_weights': True, 'weight_type': <WeightType.DI: 'dI'>, 'weight_factor': 1.0, 'weight_percent': 1.0, 'full_fit': True, '__dict__': <attribute '__dict__' of 'MaxEntParameters' objects>, '__weakref__': <attribute '__weakref__' of 'MaxEntParameters' objects>, '__doc__': "MaxEntParameters(qmin: float = 0.0, qmax: float = 0.1, dmin: float = 10.0, dmax: float = 1000.0, num_bins: int = 100, log_binning: bool = True, model: str = 'ellipsoid', aspect_ratio: float = 1.0, contrast: float = 1.0, sky_background: float = 1e-06, max_iterations: int = 100, use_weights: bool = True, weight_type: sas.qtgui.Perspectives.SizeDistribution.SizeDistributionUtils.WeightType = <WeightType.DI: 'dI'>, weight_factor: float = 1.0, weight_percent: float = 1.0, full_fit: bool = True)", '__dataclass_params__': _DataclassParams(init=True,repr=True,eq=True,order=False,unsafe_hash=False,frozen=False), '__dataclass_fields__': {'qmin': Field(name='qmin',type=<class 'float'>,default=0.0,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'qmax': Field(name='qmax',type=<class 'float'>,default=0.1,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'dmin': Field(name='dmin',type=<class 'float'>,default=10.0,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'dmax': Field(name='dmax',type=<class 'float'>,default=1000.0,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'num_bins': Field(name='num_bins',type=<class 'int'>,default=100,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'log_binning': Field(name='log_binning',type=<class 'bool'>,default=True,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'model': Field(name='model',type=<class 'str'>,default='ellipsoid',default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'aspect_ratio': Field(name='aspect_ratio',type=<class 'float'>,default=1.0,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'contrast': Field(name='contrast',type=<class 'float'>,default=1.0,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'sky_background': Field(name='sky_background',type=<class 'float'>,default=1e-06,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'max_iterations': Field(name='max_iterations',type=<class 'int'>,default=100,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'use_weights': Field(name='use_weights',type=<class 'bool'>,default=True,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'weight_type': Field(name='weight_type',type=<enum 'WeightType'>,default=<WeightType.DI: 'dI'>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'weight_factor': Field(name='weight_factor',type=<class 'float'>,default=1.0,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'weight_percent': Field(name='weight_percent',type=<class 'float'>,default=1.0,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'full_fit': Field(name='full_fit',type=<class 'bool'>,default=True,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD)}, '__init__': <function MaxEntParameters.__init__>, '__repr__': <function MaxEntParameters.__repr__>, '__eq__': <function MaxEntParameters.__eq__>, '__hash__': None, '__match_args__': ('qmin', 'qmax', 'dmin', 'dmax', 'num_bins', 'log_binning', 'model', 'aspect_ratio', 'contrast', 'sky_background', 'max_iterations', 'use_weights', 'weight_type', 'weight_factor', 'weight_percent', 'full_fit')})
__doc__ = "MaxEntParameters(qmin: float = 0.0, qmax: float = 0.1, dmin: float = 10.0, dmax: float = 1000.0, num_bins: int = 100, log_binning: bool = True, model: str = 'ellipsoid', aspect_ratio: float = 1.0, contrast: float = 1.0, sky_background: float = 1e-06, max_iterations: int = 100, use_weights: bool = True, weight_type: sas.qtgui.Perspectives.SizeDistribution.SizeDistributionUtils.WeightType = <WeightType.DI: 'dI'>, weight_factor: float = 1.0, weight_percent: float = 1.0, full_fit: bool = True)"
__eq__(other)

Return self==value.

__hash__ = None
__init__(qmin: float = 0.0, qmax: float = 0.1, dmin: float = 10.0, dmax: float = 1000.0, num_bins: int = 100, log_binning: bool = True, model: str = 'ellipsoid', aspect_ratio: float = 1.0, contrast: float = 1.0, sky_background: float = 1e-06, max_iterations: int = 100, use_weights: bool = True, weight_type: WeightType = WeightType.DI, weight_factor: float = 1.0, weight_percent: float = 1.0, full_fit: bool = True) → None
__match_args__ = ('qmin', 'qmax', 'dmin', 'dmax', 'num_bins', 'log_binning', 'model', 'aspect_ratio', 'contrast', 'sky_background', 'max_iterations', 'use_weights', 'weight_type', 'weight_factor', 'weight_percent', 'full_fit')
__module__ = 'sas.qtgui.Perspectives.SizeDistribution.SizeDistributionUtils'
__repr__()

Return repr(self).

__weakref__

list of weak references to the object

aspect_ratio: float = 1.0
contrast: float = 1.0
dmax: float = 1000.0
dmin: float = 10.0
full_fit: bool = True
log_binning: bool = True
max_iterations: int = 100
model: str = 'ellipsoid'
num_bins: int = 100
qmax: float = 0.1
qmin: float = 0.0
sky_background: float = 1e-06
use_weights: bool = True
weight_factor: float = 1.0
weight_percent: float = 1.0
weight_type: WeightType = 'dI'
class sas.qtgui.Perspectives.SizeDistribution.SizeDistributionUtils.MaxEntResult(convergences: list[bool], num_iters: list[int], chisq: float, bins: list[float], bin_mag: list[float], bin_diff: list[float], bin_err: list[float], data_max_ent: sasdata.dataloader.data_info.Data1D, statistics: dict)

Bases: object

__annotations__ = {'bin_diff': list[float], 'bin_err': list[float], 'bin_mag': list[float], 'bins': list[float], 'chisq': <class 'float'>, 'convergences': list[bool], 'data_max_ent': <class 'sasdata.dataloader.data_info.Data1D'>, 'num_iters': list[int], 'statistics': <class 'dict'>}
__dataclass_fields__ = {'bin_diff': Field(name='bin_diff',type=list[float],default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'bin_err': Field(name='bin_err',type=list[float],default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'bin_mag': Field(name='bin_mag',type=list[float],default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'bins': Field(name='bins',type=list[float],default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'chisq': Field(name='chisq',type=<class 'float'>,default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'convergences': Field(name='convergences',type=list[bool],default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'data_max_ent': Field(name='data_max_ent',type=<class 'sasdata.dataloader.data_info.Data1D'>,default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'num_iters': Field(name='num_iters',type=list[int],default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'statistics': Field(name='statistics',type=<class 'dict'>,default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD)}
__dataclass_params__ = _DataclassParams(init=True,repr=True,eq=True,order=False,unsafe_hash=False,frozen=False)
__dict__ = mappingproxy({'__module__': 'sas.qtgui.Perspectives.SizeDistribution.SizeDistributionUtils', '__annotations__': {'convergences': list[bool], 'num_iters': list[int], 'chisq': <class 'float'>, 'bins': list[float], 'bin_mag': list[float], 'bin_diff': list[float], 'bin_err': list[float], 'data_max_ent': <class 'sasdata.dataloader.data_info.Data1D'>, 'statistics': <class 'dict'>}, '__dict__': <attribute '__dict__' of 'MaxEntResult' objects>, '__weakref__': <attribute '__weakref__' of 'MaxEntResult' objects>, '__doc__': 'MaxEntResult(convergences: list[bool], num_iters: list[int], chisq: float, bins: list[float], bin_mag: list[float], bin_diff: list[float], bin_err: list[float], data_max_ent: sasdata.dataloader.data_info.Data1D, statistics: dict)', '__dataclass_params__': _DataclassParams(init=True,repr=True,eq=True,order=False,unsafe_hash=False,frozen=False), '__dataclass_fields__': {'convergences': Field(name='convergences',type=list[bool],default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'num_iters': Field(name='num_iters',type=list[int],default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'chisq': Field(name='chisq',type=<class 'float'>,default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'bins': Field(name='bins',type=list[float],default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'bin_mag': Field(name='bin_mag',type=list[float],default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'bin_diff': Field(name='bin_diff',type=list[float],default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'bin_err': Field(name='bin_err',type=list[float],default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'data_max_ent': Field(name='data_max_ent',type=<class 'sasdata.dataloader.data_info.Data1D'>,default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'statistics': Field(name='statistics',type=<class 'dict'>,default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD)}, '__init__': <function MaxEntResult.__init__>, '__repr__': <function MaxEntResult.__repr__>, '__eq__': <function MaxEntResult.__eq__>, '__hash__': None, '__match_args__': ('convergences', 'num_iters', 'chisq', 'bins', 'bin_mag', 'bin_diff', 'bin_err', 'data_max_ent', 'statistics')})
__doc__ = 'MaxEntResult(convergences: list[bool], num_iters: list[int], chisq: float, bins: list[float], bin_mag: list[float], bin_diff: list[float], bin_err: list[float], data_max_ent: sasdata.dataloader.data_info.Data1D, statistics: dict)'
__eq__(other)

Return self==value.

__hash__ = None
__init__(convergences: list[bool], num_iters: list[int], chisq: float, bins: list[float], bin_mag: list[float], bin_diff: list[float], bin_err: list[float], data_max_ent: Data1D, statistics: dict) → None
__match_args__ = ('convergences', 'num_iters', 'chisq', 'bins', 'bin_mag', 'bin_diff', 'bin_err', 'data_max_ent', 'statistics')
__module__ = 'sas.qtgui.Perspectives.SizeDistribution.SizeDistributionUtils'
__repr__()

Return repr(self).

__weakref__

list of weak references to the object

bin_diff: list[float]
bin_err: list[float]
bin_mag: list[float]
bins: list[float]
chisq: float
convergences: list[bool]
data_max_ent: Data1D
num_iters: list[int]
statistics: dict
class sas.qtgui.Perspectives.SizeDistribution.SizeDistributionUtils.WeightType(value)

Bases: StrEnum

DI = 'dI'
NONE = 'None'
PERCENT_I = 'percentI'
SQRT_I = 'sqrt(I Data)'
__doc__ = None
__format__(format_spec, /)

Return a formatted version of the string as described by format_spec.

__module__ = 'sas.qtgui.Perspectives.SizeDistribution.SizeDistributionUtils'
__new__(value)
__str__()

Return str(self).

_generate_next_value_(start, count, last_values)

Return the lower-cased version of the member name.

Module contents