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# Source code for silx.gui.fit.FitWidget

    # /*##########################################################################
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    # Copyright (c) 2004-2023 European Synchrotron Radiation Facility
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    # This file is part of the PyMca X-ray Fluorescence Toolkit developed at
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    """This module provides a widget designed to configure and run a fitting
    process with constraints on parameters.

    The main class is :class:`FitWidget`. It relies on
    :mod:`silx.math.fit.fitmanager`, which relies on :func:`silx.math.fit.leastsq`.

    The user can choose between functions before running the fit. These function can
    be user defined, or by default are loaded from
    :mod:`silx.math.fit.fittheories`.
    """

    __authors__ = ["V.A. Sole", "P. Knobel"]
    __license__ = "MIT"
    __date__ = "17/07/2018"

    import logging
    import sys
    import traceback

    from silx.math.fit import fittheories
    from silx.math.fit import fitmanager, functions
    from silx.gui import qt
    from .FitWidgets import (
        FitActionsButtons,
        FitStatusLines,
        FitConfigWidget,
        ParametersTab,
    )
    from .FitConfig import getFitConfigDialog
    from .BackgroundWidget import getBgDialog, BackgroundDialog

    DEBUG = 0
    _logger = logging.getLogger(__name__)

    __authors__ = ["V.A. Sole", "P. Knobel"]
    __license__ = "MIT"
    __date__ = "30/11/2016"

    class FitWidget(qt.QWidget):
        """This widget can be used to configure, run and display results of a
        fitting process.

        The standard steps for using this widget is to initialize it, then load
        the data to be fitted.

        Optionally, you can also load user defined fit theories. If you skip this
        step, a series of default fit functions will be presented (gaussian-like
        functions), and you can later load your custom fit theories from an
        external file using the GUI.

        A fit theory is a fit function and its associated features:

          - estimation function,
          - list of parameter names
          - numerical derivative algorithm
          - configuration widget

        Once the widget is up and running, the user may select a fit theory and a
        background theory, change configuration parameters specific to the theory
        run the estimation, set constraints on parameters and run the actual fit.

        The results are displayed in a table.

        .. image:: img/FitWidget.png
        """

        sigFitWidgetSignal = qt.Signal(object)
        """This signal is emitted by the estimation and fit methods.
        It carries a dictionary with two items:

            - *event*: one of the following strings

                - *EstimateStarted*,
                - *FitStarted*
                - *EstimateFinished*,
                - *FitFinished*
                - *EstimateFailed*
                - *FitFailed*

            - *data*: None, or fit/estimate results (see documentation for
              :attr:`silx.math.fit.fitmanager.FitManager.fit_results`)
        """

        def __init__(
            self,
            parent=None,
            title=None,
            fitmngr=None,
            enableconfig=True,
            enablestatus=True,
            enablebuttons=True,
        ):
            """

            :param parent: Parent widget
            :param title: Window title
            :param fitmngr: User defined instance of
                :class:`silx.math.fit.fitmanager.FitManager`, or ``None``
            :param enableconfig: If ``True``, activate widgets to modify the fit
                configuration (select between several fit functions or background
                functions, apply global constraints, peak search parameters…)
            :param enablestatus: If ``True``, add a fit status widget, to display
                a message when fit estimation is available and when fit results
                are available, as well as a measure of the fit error.
            :param enablebuttons: If ``True``, add buttons to run estimation and
                fitting.
            """
            if title is None:
                title = "FitWidget"
            qt.QWidget.__init__(self, parent)

            self.setWindowTitle(title)
            layout = qt.QVBoxLayout(self)

            self.fitmanager = self._setFitManager(fitmngr)
            """Instance of :class:`FitManager`.
            This is the underlying data model of this FitWidget.

            If no custom theories are defined, the default ones from
            :mod:`silx.math.fit.fittheories` are imported.
            """

            # reference fitmanager.configure method for direct access
            self.configure = self.fitmanager.configure
            self.fitconfig = self.fitmanager.fitconfig

            self.configdialogs = {}
            """This dictionary defines the fit configuration widgets
            associated with the fit theories in :attr:`fitmanager.theories`

            Keys must correspond to existing theory names, i.e. existing keys
            in :attr:`fitmanager.theories`.

            Values must be instances of QDialog widgets with an additional
            *output* attribute, a dictionary storing configuration parameters
            interpreted by the corresponding fit theory.

            The dialog can also define a *setDefault* method to initialize the
            widget values with values in a dictionary passed as a parameter.
            This will be executed first.

            In case the widget does not actually inherit :class:`QDialog`, it
            must at least implement the following methods (executed in this
            particular order):

                - :meth:`show`: should cause the widget to become visible to the
                  user)
                - :meth:`exec`: should run while the user is interacting with the
                  widget, interrupting the rest of the program. It should
                  typically end (*return*) when the user clicks an *OK*
                  or a *Cancel* button.
                - :meth:`result`: must return ``True`` if the new configuration in
                  attribute :attr:`output` is to be accepted (user clicked *OK*),
                  or return ``False`` if :attr:`output` is to be rejected (user
                  clicked *Cancel*)

            To associate a custom configuration widget with a fit theory, use
            :meth:`associateConfigDialog`. E.g.::

                fw = FitWidget()
                my_config_widget = MyGaussianConfigWidget(parent=fw)
                fw.associateConfigDialog(theory_name="Gaussians",
                                         config_widget=my_config_widget)
            """

            self.bgconfigdialogs = {}
            """Same as :attr:`configdialogs`, except that the widget is associated
            with a background theory in :attr:`fitmanager.bgtheories`"""

            self._associateDefaultConfigDialogs()

            self.guiConfig = None
            """Configuration widget at the top of FitWidget, to select
            fit function, background function, and open an advanced
            configuration dialog."""

            self.guiParameters = ParametersTab(self)
            """Table widget for display of fit parameters and constraints"""

            if enableconfig:
                self.guiConfig = FitConfigWidget(self)
                """Function selector and configuration widget"""

                self.guiConfig.FunConfigureButton.clicked.connect(
                    self.__funConfigureGuiSlot
                )
                self.guiConfig.BgConfigureButton.clicked.connect(self.__bgConfigureGuiSlot)

                self.guiConfig.WeightCheckBox.setChecked(
                    self.fitconfig.get("WeightFlag", False)
                )
                self.guiConfig.WeightCheckBox.stateChanged[int].connect(self.weightEvent)

                if qt.BINDING == "PyQt5":
                    self.guiConfig.BkgComBox.activated[str].connect(self.bkgEvent)
                    self.guiConfig.FunComBox.activated[str].connect(self.funEvent)
                else:  # Qt6
                    self.guiConfig.BkgComBox.textActivated.connect(self.bkgEvent)
                    self.guiConfig.FunComBox.textActivated.connect(self.funEvent)

                self._populateFunctions()

                layout.addWidget(self.guiConfig)

            layout.addWidget(self.guiParameters)

            if enablestatus:
                self.guistatus = FitStatusLines(self)
                """Status bar"""
                layout.addWidget(self.guistatus)

            if enablebuttons:
                self.guibuttons = FitActionsButtons(self)
                """Widget with estimate, start fit and dismiss buttons"""
                self.guibuttons.EstimateButton.clicked.connect(self.estimate)
                self.guibuttons.EstimateButton.setEnabled(False)
                self.guibuttons.StartFitButton.clicked.connect(self.startFit)
                self.guibuttons.StartFitButton.setEnabled(False)
                self.guibuttons.DismissButton.clicked.connect(self.dismiss)
                layout.addWidget(self.guibuttons)

        def _setFitManager(self, fitinstance):
            """Initialize a :class:`FitManager` instance, to be assigned to
            :attr:`fitmanager`, or use a custom FitManager instance.

            :param fitinstance: Existing instance of FitManager, possibly
                customized by the user, or None to load a default instance."""
            if isinstance(fitinstance, fitmanager.FitManager):
                # customized
                fitmngr = fitinstance
            else:
                # initialize default instance
                fitmngr = fitmanager.FitManager()

            # initialize the default fitting functions in case
            # none is present
            if not len(fitmngr.theories):
                fitmngr.loadtheories(fittheories)

            return fitmngr

        def _associateDefaultConfigDialogs(self):
            """Fill :attr:`bgconfigdialogs` and :attr:`configdialogs` by calling
            :meth:`associateConfigDialog` with default config dialog widgets.
            """
            # associate silx.gui.fit.FitConfig with all theories
            # Users can later associate their own custom dialogs to
            # replace the default.
            configdialog = getFitConfigDialog(parent=self, default=self.fitconfig)
            for theory in self.fitmanager.theories:
                self.associateConfigDialog(theory, configdialog)
            for bgtheory in self.fitmanager.bgtheories:
                self.associateConfigDialog(
                    bgtheory, configdialog, theory_is_background=True
                )

            # associate silx.gui.fit.BackgroundWidget with Strip and Snip
            bgdialog = getBgDialog(parent=self, default=self.fitconfig)
            for bgtheory in ["Strip", "Snip"]:
                if bgtheory in self.fitmanager.bgtheories:
                    self.associateConfigDialog(
                        bgtheory, bgdialog, theory_is_background=True
                    )

        def _populateFunctions(self):
            """Fill combo-boxes with fit theories and background theories
            loaded by :attr:`fitmanager`.
            Run :meth:`fitmanager.configure` to ensure the custom configuration
            of the selected theory has been loaded into :attr:`fitconfig`"""
            for theory_name in self.fitmanager.bgtheories:
                self.guiConfig.BkgComBox.addItem(theory_name)
                self.guiConfig.BkgComBox.setItemData(
                    self.guiConfig.BkgComBox.findText(theory_name),
                    self.fitmanager.bgtheories[theory_name].description,
                    qt.Qt.ToolTipRole,
                )

            for theory_name in self.fitmanager.theories:
                self.guiConfig.FunComBox.addItem(theory_name)
                self.guiConfig.FunComBox.setItemData(
                    self.guiConfig.FunComBox.findText(theory_name),
                    self.fitmanager.theories[theory_name].description,
                    qt.Qt.ToolTipRole,
                )

            # - activate selected fit theory (if any)
            #    - activate selected bg theory (if any)
            configuration = self.fitmanager.configure()
            if self.fitmanager.selectedtheory is None:
                # take the first one by default
                self.guiConfig.FunComBox.setCurrentIndex(1)
                self.funEvent(list(self.fitmanager.theories.keys())[0])
            else:
                idx = list(self.fitmanager.theories).index(self.fitmanager.selectedtheory)
                self.guiConfig.FunComBox.setCurrentIndex(idx + 1)
                self.funEvent(self.fitmanager.selectedtheory)

            if self.fitmanager.selectedbg is None:
                self.guiConfig.BkgComBox.setCurrentIndex(1)
                self.bkgEvent(list(self.fitmanager.bgtheories.keys())[0])
            else:
                idx = list(self.fitmanager.bgtheories).index(self.fitmanager.selectedbg)
                self.guiConfig.BkgComBox.setCurrentIndex(idx + 1)
                self.bkgEvent(self.fitmanager.selectedbg)

            configuration.update(self.configure())

        def setData(self, x=None, y=None, sigmay=None, xmin=None, xmax=None):
            """Set data to be fitted.

            :param x: Abscissa data. If ``None``, :attr:`xdata`` is set to
                ``numpy.array([0.0, 1.0, 2.0, ..., len(y)-1])``
            :type x: Sequence or numpy array or None
            :param y: The dependant data ``y = f(x)``. ``y`` must have the same
                shape as ``x`` if ``x`` is not ``None``.
            :type y: Sequence or numpy array or None
            :param sigmay: The uncertainties in the ``ydata`` array. These are
                used as weights in the least-squares problem.
                If ``None``, the uncertainties are assumed to be 1.
            :type sigmay: Sequence or numpy array or None
            :param xmin: Lower value of x values to use for fitting
            :param xmax: Upper value of x values to use for fitting
            """
            if y is None:
                self.guibuttons.EstimateButton.setEnabled(False)
                self.guibuttons.StartFitButton.setEnabled(False)
            else:
                self.guibuttons.EstimateButton.setEnabled(True)
                self.guibuttons.StartFitButton.setEnabled(True)
                self.fitmanager.setdata(x=x, y=y, sigmay=sigmay, xmin=xmin, xmax=xmax)
                for config_dialog in self.bgconfigdialogs.values():
                    if isinstance(config_dialog, BackgroundDialog):
                        config_dialog.setData(x, y, xmin=xmin, xmax=xmax)

        def associateConfigDialog(
            self, theory_name, config_widget, theory_is_background=False
        ):
            """Associate an instance of custom configuration dialog widget to
            a fit theory or to a background theory.

            This adds or modifies an item in the correspondence table
            :attr:`configdialogs` or :attr:`bgconfigdialogs`.

            :param str theory_name: Name of fit theory. This must be a key of dict
                :attr:`fitmanager.theories`
            :param config_widget: Custom configuration widget. See documentation
                for :attr:`configdialogs`
            :param bool theory_is_background: If flag is *True*, add dialog to
                :attr:`bgconfigdialogs` rather than :attr:`configdialogs`
                (default).
            :raise: KeyError if parameter ``theory_name`` does not match an
                existing fit theory or background theory in :attr:`fitmanager`.
            :raise: AttributeError if the widget does not implement the mandatory
                methods (*show*, *exec*, *result*, *setDefault*) or the mandatory
                attribute (*output*).
            """
            theories = (
                self.fitmanager.bgtheories
                if theory_is_background
                else self.fitmanager.theories
            )

            if theory_name not in theories:
                raise KeyError("%s does not match an existing fitmanager theory")

            if config_widget is not None:
                if not hasattr(config_widget, "exec") and not hasattr(
                    config_widget, "exec_"
                ):
                    raise AttributeError(
                        "Custom configuration widget must define exec or exec_"
                    )

                for mandatory_attr in ["show", "result", "output"]:
                    if not hasattr(config_widget, mandatory_attr):
                        raise AttributeError(
                            "Custom configuration widget must define "
                            + "attribute or method "
                            + mandatory_attr
                        )

            if theory_is_background:
                self.bgconfigdialogs[theory_name] = config_widget
            else:
                self.configdialogs[theory_name] = config_widget

        def _emitSignal(self, ddict):
            """Emit pyqtSignal after estimation completed
            (``ddict = {'event': 'EstimateFinished', 'data': fit_results}``)
            and after fit completed
            (``ddict = {'event': 'FitFinished', 'data': fit_results}``)"""
            self.sigFitWidgetSignal.emit(ddict)

        def __funConfigureGuiSlot(self):
            """Open an advanced configuration dialog widget"""
            self.__configureGui(dialog_type="function")

        def __bgConfigureGuiSlot(self):
            """Open an advanced configuration dialog widget"""
            self.__configureGui(dialog_type="background")

        def __configureGui(self, newconfiguration=None, dialog_type="function"):
            """Open an advanced configuration dialog widget to get a configuration
            dictionary, or use a supplied configuration dictionary. Call
            :meth:`configure` with this dictionary as a parameter. Update the gui
            accordingly. Reinitialize the fit results in the table and in
            :attr:`fitmanager`.

            :param newconfiguration: User supplied configuration dictionary. If ``None``,
                open a dialog widget that returns a dictionary."""
            configuration = self.configure()
            # get new dictionary
            if newconfiguration is None:
                newconfiguration = self.configureDialog(configuration, dialog_type)
            # update configuration
            configuration.update(self.configure(**newconfiguration))
            # set fit function theory
            try:
                i = 1 + list(self.fitmanager.theories.keys()).index(
                    self.fitmanager.selectedtheory
                )
                self.guiConfig.FunComBox.setCurrentIndex(i)
                self.funEvent(self.fitmanager.selectedtheory)
            except ValueError:
                _logger.error("Function not in list %s", self.fitmanager.selectedtheory)
                self.funEvent(list(self.fitmanager.theories.keys())[0])
            # current background
            try:
                i = 1 + list(self.fitmanager.bgtheories.keys()).index(
                    self.fitmanager.selectedbg
                )
                self.guiConfig.BkgComBox.setCurrentIndex(i)
                self.bkgEvent(self.fitmanager.selectedbg)
            except ValueError:
                _logger.error("Background not in list %s", self.fitmanager.selectedbg)
                self.bkgEvent(list(self.fitmanager.bgtheories.keys())[0])

            # update the Gui
            self.__initialParameters()

        def configureDialog(self, oldconfiguration, dialog_type="function"):
            """Display a dialog, allowing the user to define fit configuration
            parameters.

            By default, a common dialog is used for all fit theories. But if the
            defined a custom dialog using :meth:`associateConfigDialog`, it is
            used instead.

            :param dict oldconfiguration: Dictionary containing previous configuration
            :param str dialog_type: "function" or "background"
            :return: User defined parameters in a dictionary
            """
            newconfiguration = {}
            newconfiguration.update(oldconfiguration)

            if dialog_type == "function":
                theory = self.fitmanager.selectedtheory
                configdialog = self.configdialogs[theory]
            elif dialog_type == "background":
                theory = self.fitmanager.selectedbg
                configdialog = self.bgconfigdialogs[theory]

            # this should only happen if a user specifically associates None
            # with a theory, to have no configuration option
            if configdialog is None:
                return {}

            # update state of configdialog before showing it
            if hasattr(configdialog, "setDefault"):
                configdialog.setDefault(newconfiguration)
            configdialog.show()
            if hasattr(configdialog, "exec"):
                configdialog.exec()
            else:  # Qt5 compatibility
                configdialog.exec_()
            if configdialog.result():
                newconfiguration.update(configdialog.output)

            return newconfiguration

        def estimate(self):
            """Run parameter estimation function then emit
            :attr:`sigFitWidgetSignal` with a dictionary containing a status
            message and a list of fit parameters estimations
            in the format defined in
            :attr:`silx.math.fit.fitmanager.FitManager.fit_results`

            The emitted dictionary has an *"event"* key that can have
            following values:

                - *'EstimateStarted'*
                - *'EstimateFailed'*
                - *'EstimateFinished'*
            """
            try:
                theory_name = self.fitmanager.selectedtheory
                estimation_function = self.fitmanager.theories[theory_name].estimate
                if estimation_function is not None:
                    ddict = {"event": "EstimateStarted", "data": None}
                    self._emitSignal(ddict)
                    self.fitmanager.estimate(callback=self.fitStatus)
                else:
                    msg = qt.QMessageBox(self)
                    msg.setIcon(qt.QMessageBox.Information)
                    text = "Function does not define a way to estimate\n"
                    text += "the initial parameters. Please, fill them\n"
                    text += "yourself in the table and press Start Fit\n"
                    msg.setText(text)
                    msg.setWindowTitle("FitWidget Message")
                    msg.exec()
                    return
            except Exception as e:  # noqa (we want to catch and report all errors)
                _logger.warning("Estimate error: %s", traceback.format_exc())
                msg = qt.QMessageBox(self)
                msg.setIcon(qt.QMessageBox.Critical)
                msg.setWindowTitle("Estimate Error")
                msg.setText("Error on estimate: %s" % e)
                msg.exec()
                ddict = {"event": "EstimateFailed", "data": None}
                self._emitSignal(ddict)
                return

            self.guiParameters.fillFromFit(self.fitmanager.fit_results, view="Fit")
            self.guiParameters.removeAllViews(keep="Fit")
            ddict = {"event": "EstimateFinished", "data": self.fitmanager.fit_results}
            self._emitSignal(ddict)

        def startFit(self):
            """Run fit, then emit :attr:`sigFitWidgetSignal` with a dictionary
            containing a status message and a list of fit
            parameters results in the format defined in
            :attr:`silx.math.fit.fitmanager.FitManager.fit_results`

            The emitted dictionary has an *"event"* key that can have
            following values:

                - *'FitStarted'*
                - *'FitFailed'*
                - *'FitFinished'*
            """
            self.fitmanager.fit_results = self.guiParameters.getFitResults()
            try:
                ddict = {"event": "FitStarted", "data": None}
                self._emitSignal(ddict)
                self.fitmanager.runfit(callback=self.fitStatus)
            except Exception as e:  # noqa (we want to catch and report all errors)
                _logger.warning("Estimate error: %s", traceback.format_exc())
                msg = qt.QMessageBox(self)
                msg.setIcon(qt.QMessageBox.Critical)
                msg.setWindowTitle("Fit Error")
                msg.setText("Error on Fit: %s" % e)
                msg.exec()
                ddict = {"event": "FitFailed", "data": None}
                self._emitSignal(ddict)
                return

            self.guiParameters.fillFromFit(self.fitmanager.fit_results, view="Fit")
            self.guiParameters.removeAllViews(keep="Fit")
            ddict = {"event": "FitFinished", "data": self.fitmanager.fit_results}
            self._emitSignal(ddict)
            return

        def bkgEvent(self, bgtheory):
            """Select background theory, then reinitialize parameters"""
            bgtheory = str(bgtheory)
            if bgtheory in self.fitmanager.bgtheories:
                self.fitmanager.setbackground(bgtheory)
            else:
                functionsfile = qt.QFileDialog.getOpenFileName(
                    self,
                    "Select python module with your function(s)",
                    "",
                    "Python Files (*.py);;All Files (*)",
                )

                if len(functionsfile):
                    try:
                        self.fitmanager.loadbgtheories(functionsfile)
                    except ImportError:
                        qt.QMessageBox.critical(self, "ERROR", "Function not imported")
                        return
                    else:
                        # empty the ComboBox
                        while self.guiConfig.BkgComBox.count() > 1:
                            self.guiConfig.BkgComBox.removeItem(1)
                        # and fill it again
                        for key in self.fitmanager.bgtheories:
                            self.guiConfig.BkgComBox.addItem(str(key))

                i = 1 + list(self.fitmanager.bgtheories.keys()).index(
                    self.fitmanager.selectedbg
                )
                self.guiConfig.BkgComBox.setCurrentIndex(i)
            self.__initialParameters()

        def funEvent(self, theoryname):
            """Select a fit theory to be used for fitting. If this theory exists
            in :attr:`fitmanager`, use it. Then, reinitialize table.

            :param theoryname: Name of the fit theory to use for fitting. If this theory
                exists in :attr:`fitmanager`, use it. Else, open a file dialog to open
                a custom fit function definition file with
                :meth:`fitmanager.loadtheories`.
            """
            theoryname = str(theoryname)
            if theoryname in self.fitmanager.theories:
                self.fitmanager.settheory(theoryname)
            else:
                # open a load file dialog
                functionsfile = qt.QFileDialog.getOpenFileName(
                    self,
                    "Select python module with your function(s)",
                    "",
                    "Python Files (*.py);;All Files (*)",
                )

                if len(functionsfile):
                    try:
                        self.fitmanager.loadtheories(functionsfile)
                    except ImportError:
                        qt.QMessageBox.critical(self, "ERROR", "Function not imported")
                        return
                    else:
                        # empty the ComboBox
                        while self.guiConfig.FunComBox.count() > 1:
                            self.guiConfig.FunComBox.removeItem(1)
                        # and fill it again
                        for key in self.fitmanager.theories:
                            self.guiConfig.FunComBox.addItem(str(key))

                i = 1 + list(self.fitmanager.theories.keys()).index(
                    self.fitmanager.selectedtheory
                )
                self.guiConfig.FunComBox.setCurrentIndex(i)
            self.__initialParameters()

        def weightEvent(self, flag):
            """This is called when WeightCheckBox is clicked, to configure the
            *WeightFlag* field in :attr:`fitmanager.fitconfig` and set weights
            in the least-square problem."""
            self.configure(WeightFlag=flag)
            if flag:
                self.fitmanager.enableweight()
            else:
                # set weights back to 1
                self.fitmanager.disableweight()

        def __initialParameters(self):
            """Fill the fit parameters names with names of the parameters of
            the selected background theory and the selected fit theory.
            Initialize :attr:`fitmanager.fit_results` with these names, and
            initialize the table with them. This creates a view called "Fit"
            in :attr:`guiParameters`"""
            self.fitmanager.parameter_names = []
            self.fitmanager.fit_results = []
            for pname in self.fitmanager.bgtheories[self.fitmanager.selectedbg].parameters:
                self.fitmanager.parameter_names.append(pname)
                self.fitmanager.fit_results.append(
                    {
                        "name": pname,
                        "estimation": 0,
                        "group": 0,
                        "code": "FREE",
                        "cons1": 0,
                        "cons2": 0,
                        "fitresult": 0.0,
                        "sigma": 0.0,
                        "xmin": None,
                        "xmax": None,
                    }
                )
            if self.fitmanager.selectedtheory is not None:
                theory = self.fitmanager.selectedtheory
                for pname in self.fitmanager.theories[theory].parameters:
                    self.fitmanager.parameter_names.append(pname + "1")
                    self.fitmanager.fit_results.append(
                        {
                            "name": pname + "1",
                            "estimation": 0,
                            "group": 1,
                            "code": "FREE",
                            "cons1": 0,
                            "cons2": 0,
                            "fitresult": 0.0,
                            "sigma": 0.0,
                            "xmin": None,
                            "xmax": None,
                        }
                    )

            self.guiParameters.fillFromFit(self.fitmanager.fit_results, view="Fit")

        def fitStatus(self, data):
            """Set *status* and *chisq* in status bar"""
            if "chisq" in data:
                if data["chisq"] is None:
                    self.guistatus.ChisqLine.setText(" ")
                else:
                    chisq = data["chisq"]
                    self.guistatus.ChisqLine.setText("%6.2f" % chisq)

            if "status" in data:
                status = data["status"]
                self.guistatus.StatusLine.setText(str(status))

        def dismiss(self):
            """Close FitWidget"""
            self.close()

    if __name__ == "__main__":
        import numpy

        x = numpy.arange(1500).astype(numpy.float64)
        constant_bg = 3.14

        p = [
            1000,
            100.0,
            30.0,
            500,
            300.0,
            25.0,
            1700,
            500.0,
            35.0,
            750,
            700.0,
            30.0,
            1234,
            900.0,
            29.5,
            302,
            1100.0,
            30.5,
            75,
            1300.0,
            21.0,
        ]
        y = functions.sum_gauss(x, *p) + constant_bg

        a = qt.QApplication(sys.argv)
        w = FitWidget()
        w.setData(x=x, y=y)
        w.show()
        a.exec()