# -*- coding: utf-8 -*-
# Copyright 2007-2016 The HyperSpy developers
#
# This file is part of  HyperSpy.
#
#  HyperSpy is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
#  HyperSpy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with  HyperSpy.  If not, see <http://www.gnu.org/licenses/>.

import copy
import warnings
import logging

from hyperspy.models.model1d import Model1D
from hyperspy.components1d import EELSCLEdge
from hyperspy.components1d import PowerLaw
from hyperspy import components1d
from hyperspy._signals.eels import EELSSpectrum

_logger = logging.getLogger(__name__)


class EELSModel(Model1D):

    """Build an EELS model

    Parameters
    ----------
    spectrum : a Signal1D (or any Signal1D subclass) instance
    auto_background : boolean
        If True, and if spectrum is an EELS instance adds automatically
        a powerlaw to the model and estimate the parameters by the
        two-area method.
    auto_add_edges : boolean
        If True, and if spectrum is an EELS instance, it will
        automatically add the ionization edges as defined in the
        Signal1D instance. Adding a new element to the spectrum using
        the components.EELSSpectrum.add_elements method automatically
        add the corresponding ionisation edges to the model.
    ll : {None, EELSSpectrum}
        If an EELSSPectrum is provided, it will be assumed that it is
        a low-loss EELS spectrum, and it will be used to simulate the
        effect of multiple scattering by convolving it with the EELS
        spectrum.
    GOS : {'hydrogenic', 'Hartree-Slater', None}
        The GOS to use when auto adding core-loss EELS edges.
        If None it will use the Hartree-Slater GOS if
        they are available, otherwise it will use the hydrogenic GOS.
    dictionary : {dict, None}
        A dictionary to be used to recreate a model. Usually generated using
        :meth:`hyperspy.model.as_dictionary`

    """

    def __init__(self, signal1D, auto_background=True,
                 auto_add_edges=True, ll=None,
                 GOS=None, dictionary=None):
        Model1D.__init__(self, signal1D)

        # When automatically setting the fine structure energy regions,
        # the fine structure of an EELS edge component is automatically
        # disable if the next ionisation edge onset distance to the
        # higher energy side of the fine structure region is lower that
        # the value of this parameter
        self._min_distance_between_edges_for_fine_structure = 0
        self._preedge_safe_window_width = 2
        self.signal1D = signal1D
        self._suspend_auto_fine_structure_width = False
        self.convolved = False
        self.low_loss = ll
        self.GOS = GOS
        self.edges = []
        self._background_components = []
        if dictionary is not None:
            auto_background = False
            auto_add_edges = False
            self._load_dictionary(dictionary)

        if auto_background is True:
            background = PowerLaw()
            self.append(background)

        if self.signal.subshells and auto_add_edges is True:
            self._add_edges_from_subshells_names()

    @property
    def signal1D(self):
        return self._signal

    @signal1D.setter
    def signal1D(self, value):
        if isinstance(value, EELSSpectrum):
            self._signal = value
        else:
            raise ValueError(
                "This attribute can only contain an EELSSpectrum "
                "but an object of type %s was provided" %
                str(type(value)))

    def append(self, component):
        super(EELSModel, self).append(component)
        if isinstance(component, EELSCLEdge):
            tem = self.signal.metadata.Acquisition_instrument.TEM
            component.set_microscope_parameters(
                E0=tem.beam_energy,
                alpha=tem.convergence_angle,
                beta=tem.Detector.EELS.collection_angle,
                energy_scale=self.axis.scale)
            component.energy_scale = self.axis.scale
            component._set_fine_structure_coeff()
        self._classify_components()

    append.__doc__ = Model1D.append.__doc__

    def remove(self, component):
        super(EELSModel, self).remove(component)
        self._classify_components()
    remove.__doc__ = Model1D.remove.__doc__

    def _classify_components(self):
        """Classify components between background and ionization edge
        components.

        This method should be called everytime that components are added and
        removed. An ionization edge becomes background when its onset falls to
        the left of the first non-masked energy channel. The ionization edges
        are stored in a list in the `edges` attribute. They are sorted by
        increasing `onset_energy`. The background components are stored in
        `_background_components`.

        """
        self.edges = []
        self._background_components = []
        for component in self:
            if isinstance(component, EELSCLEdge):
                if component.onset_energy.value < \
                        self.axis.axis[self.channel_switches][0]:
                    component.isbackground = True
                if component.isbackground is not True:
                    self.edges.append(component)
                else:
                    component.fine_structure_active = False
                    component.fine_structure_coeff.free = False
            elif (isinstance(component, PowerLaw) or
                  component.isbackground is True):
                self._background_components.append(component)

        if self.edges:
            self.edges.sort(key=EELSCLEdge._onset_energy)
            self.resolve_fine_structure()
        if len(self._background_components) > 1:
            self._backgroundtype = "mix"
        elif len(self._background_components) == 1:
            self._backgroundtype = \
                self._background_components[0].__repr__()
            bg = self._background_components[0]
            if isinstance(bg, PowerLaw) and self.edges and not \
               bg.A.map["is_set"].any():
                self.two_area_background_estimation()

    @property
    def _active_edges(self):
        return [edge for edge in self.edges if edge.active]

    @property
    def _active_background_components(self):
        return [bc for bc in self._background_components if bc.active]

    def _add_edges_from_subshells_names(self, e_shells=None):
        """Create the Edge instances and configure them appropiately
        Parameters
        ----------
        e_shells : list of strings
        """
        if self.signal._are_microscope_parameters_missing():
            raise ValueError(
                "The required microscope parameters are not defined in "
                "the EELS spectrum signal metadata. Use "
                "``set_microscope_parameters`` to set them."
            )
        if e_shells is None:
            e_shells = list(self.signal.subshells)
        e_shells.sort()
        master_edge = EELSCLEdge(e_shells.pop(), self.GOS)
        # If self.GOS was None, the GOS is set by eels_cl_edge so
        # we reassing the value of self.GOS
        self.GOS = master_edge.GOS._name
        self.append(master_edge)
        element = master_edge.element
        while len(e_shells) > 0:
            next_element = e_shells[-1].split('_')[0]
            if next_element != element:
                # New master edge
                self._add_edges_from_subshells_names(e_shells=e_shells)
            elif self.GOS == 'hydrogenic':
                # The hydrogenic GOS includes all the L subshells in one
                # so we get rid of the others
                e_shells.pop()
            else:
                # Add the other subshells of the same element
                # and couple their intensity and onset_energy to that of the
                # master edge
                edge = EELSCLEdge(e_shells.pop(), GOS=self.GOS)

                edge.intensity.twin = master_edge.intensity
                edge.onset_energy.twin = master_edge.onset_energy
                edge.onset_energy.twin_function_expr = "x + {}".format(
                    (edge.GOS.onset_energy - master_edge.GOS.onset_energy))
                edge.free_onset_energy = False
                self.append(edge)

    def resolve_fine_structure(
            self,
            preedge_safe_window_width=2,
            i1=0):
        """Adjust the fine structure of all edges to avoid overlapping

        This function is called automatically everytime the position of an edge
        changes

        Parameters
        ----------
        preedge_safe_window_width : float
            minimum distance between the fine structure of an ionization edge
            and that of the following one. Default 2 (eV).

        """

        if self._suspend_auto_fine_structure_width is True:
            return

        if not self._active_edges:
            return

        while (self._active_edges[i1].fine_structure_active is False and
               i1 < len(self._active_edges) - 1):
            i1 += 1
        if i1 < len(self._active_edges) - 1:
            i2 = i1 + 1
            while (self._active_edges[i2].fine_structure_active is False and
                    i2 < len(self._active_edges) - 1):
                i2 += 1
            if self._active_edges[i2].fine_structure_active is True:
                distance_between_edges = (
                    self._active_edges[i2].onset_energy.value -
                    self._active_edges[i1].onset_energy.value)
                if (self._active_edges[i1].fine_structure_width >
                        distance_between_edges -
                        self._preedge_safe_window_width):
                    min_d = self._min_distance_between_edges_for_fine_structure
                    if (distance_between_edges -
                            self._preedge_safe_window_width) <= min_d:
                        _logger.info((
                            "Automatically deactivating the fine structure "
                            "of edge number %d to avoid conflicts with edge "
                            "number %d") % (i2 + 1, i1 + 1))
                        self._active_edges[i2].fine_structure_active = False
                        self._active_edges[
                            i2].fine_structure_coeff.free = False
                        self.resolve_fine_structure(i1=i2)
                    else:
                        new_fine_structure_width = (
                            distance_between_edges -
                            self._preedge_safe_window_width)
                        _logger.info((
                            "Automatically changing the fine structure "
                            "width of edge %d from %s eV to %s eV to avoid "
                            "conflicts with edge number %d") % (
                            i1 + 1,
                            self._active_edges[i1].fine_structure_width,
                            new_fine_structure_width,
                            i2 + 1))
                        self._active_edges[i1].fine_structure_width = \
                            new_fine_structure_width
                        self.resolve_fine_structure(i1=i2)
                else:
                    self.resolve_fine_structure(i1=i2)
        else:
            return

    def fit(self, fitter=None, method='ls', grad=False,
            bounded=False, ext_bounding=False, update_plot=False,
            kind='std', **kwargs):
        """Fits the model to the experimental data

        Parameters
        ----------
        fitter : {None, "leastsq", "odr", "mpfit", "fmin"}
            The optimizer to perform the fitting. If None the fitter
            defined in the Preferences is used. leastsq is the most
            stable but it does not support bounding. mpfit supports
            bounding. fmin is the only one that supports
            maximum likelihood estimation, but it is less robust than
            the Levenberg–Marquardt based leastsq and mpfit, and it is
            better to use it after one of them to refine the estimation.
        method : {'ls', 'ml'}
            Choose 'ls' (default) for least squares and 'ml' for
            maximum-likelihood estimation. The latter only works with
            fitter = 'fmin'.
        grad : bool
            If True, the analytical gradient is used if defined to
            speed up the estimation.
        ext_bounding : bool
            If True, enforce bounding by keeping the value of the
            parameters constant out of the defined bounding area.
        bounded : bool
            If True performs bounded optimization if the fitter
            supports it. Currently only mpfit support bounding.
        update_plot : bool
            If True, the plot is updated during the optimization
            process. It slows down the optimization but it permits
            to visualize the optimization evolution.
        kind : {'std', 'smart'}
            If 'std' (default) performs standard fit. If 'smart'
            performs smart_fit

        **kwargs : key word arguments
            Any extra key word argument will be passed to the chosen
            fitter

        See Also
        --------
        multifit, smart_fit

        """
        if kind == 'smart':
            self.smart_fit(fitter=fitter,
                           method=method,
                           grad=grad,
                           bounded=bounded,
                           ext_bounding=ext_bounding,
                           update_plot=update_plot,
                           **kwargs)
        elif kind == 'std':
            Model1D.fit(self,
                        fitter=fitter,
                        method=method,
                        grad=grad,
                        bounded=bounded,
                        ext_bounding=ext_bounding,
                        update_plot=update_plot,
                        **kwargs)
        else:
            raise ValueError('kind must be either \'std\' or \'smart\'.'
                             '\'%s\' provided.' % kind)

    def smart_fit(self, start_energy=None, **kwargs):
        """ Fits everything in a cascade style.

        Parameters
        ----------

        start_energy : {float, None}
            If float, limit the range of energies from the left to the
            given value.
        **kwargs : key word arguments
            Any extra key word argument will be passed to
            the fit method. See the fit method documentation for
            a list of valid arguments.

        See Also
        --------
        fit, multifit

        """

        # Fit background
        self.fit_background(start_energy, **kwargs)

        # Fit the edges
        for i in range(0, len(self._active_edges)):
            self._fit_edge(i, start_energy, **kwargs)

    def _get_first_ionization_edge_energy(self, start_energy=None):
        """Calculate the first ionization edge energy.

        Returns
        -------
        iee : float or None
            The first ionization edge energy or None if no edge is defined in
            the model.

        """
        if not self._active_edges:
            return None
        start_energy = self._get_start_energy(start_energy)
        iee_list = [edge.onset_energy.value for edge in self._active_edges
                    if edge.onset_energy.value > start_energy]
        iee = min(iee_list) if iee_list else None
        return iee

    def _get_start_energy(self, start_energy=None):
        E0 = self.axis.axis[self.channel_switches][0]
        if not start_energy or start_energy < E0:
            start_energy = E0
        return start_energy

    def fit_background(self, start_energy=None, only_current=True, **kwargs):
        """Fit the background to the first active ionization edge
        in the energy range.

        Parameters
        ----------
        start_energy : {float, None}, optional
            If float, limit the range of energies from the left to the
            given value. Default None.
        only_current : bool, optional
            If True, only fit the background at the current coordinates.
            Default True.
        **kwargs : extra key word arguments
            All extra key word arguments are passed to fit or
            multifit.

        """

        # If there is no active background compenent do nothing
        if not self._active_background_components:
            return
        iee = self._get_first_ionization_edge_energy(start_energy=start_energy)
        if iee is not None:
            to_disable = [edge for edge in self._active_edges
                          if edge.onset_energy.value >= iee]
            E2 = iee - self._preedge_safe_window_width
            self.disable_edges(to_disable)
        else:
            E2 = None
        self.set_signal_range(start_energy, E2)
        if only_current:
            self.fit(**kwargs)
        else:
            self.multifit(**kwargs)
        self.channel_switches = copy.copy(self.backup_channel_switches)
        if iee is not None:
            self.enable_edges(to_disable)

    def two_area_background_estimation(self, E1=None, E2=None, powerlaw=None):
        """Estimates the parameters of a power law background with the two
        area method.

        Parameters
        ----------
        E1 : float
        E2 : float
        powerlaw : PowerLaw component or None
            If None, it will try to guess the right component from the
            background components of the model

        """
        if powerlaw is None:
            for component in self._active_background_components:
                if isinstance(component, components1d.PowerLaw):
                    if powerlaw is None:
                        powerlaw = component
                    else:
                        _logger.warning(
                            'There are more than two power law '
                            'background components defined in this model, '
                            'please use the powerlaw keyword to specify one'
                            ' of them')
                        return
                else:  # No power law component
                    return

        ea = self.axis.axis[self.channel_switches]
        E1 = self._get_start_energy(E1)
        if E2 is None:
            E2 = self._get_first_ionization_edge_energy(start_energy=E1)
            if E2 is None:
                E2 = ea[-1]
            else:
                E2 = E2 - \
                    self._preedge_safe_window_width

        if not powerlaw.estimate_parameters(
                self.signal, E1, E2, only_current=False):
            _logger.warning(
                "The power law background parameters could not "
                "be estimated.\n"
                "Try choosing a different energy range for the estimation")
            return

    def _fit_edge(self, edgenumber, start_energy=None, **kwargs):
        backup_channel_switches = self.channel_switches.copy()
        ea = self.axis.axis[self.channel_switches]
        if start_energy is None:
            start_energy = ea[0]
        # Declare variables
        active_edges = self._active_edges
        edge = active_edges[edgenumber]
        if (edge.intensity.twin is not None or
                edge.active is False or
                edge.onset_energy.value < start_energy or
                edge.onset_energy.value > ea[-1]):
            return 1
        # Fitting edge 'edge.name'
        last_index = len(self._active_edges) - 1    # Last edge index
        i = 1
        twins = []
        while edgenumber + i <= last_index and (
                active_edges[edgenumber + i].intensity.twin is not None or
                active_edges[edgenumber + i].active is False):
            if active_edges[edgenumber + i].intensity.twin is not None:
                twins.append(self._active_edges[edgenumber + i])
            i += 1
        if (edgenumber + i) > last_index:
            nextedgeenergy = ea[-1]
        else:
            nextedgeenergy = (
                active_edges[edgenumber + i].onset_energy.value -
                self._preedge_safe_window_width)

        # Backup the fsstate
        to_activate_fs = []
        for edge_ in [edge, ] + twins:
            if (edge_.fine_structure_active is True and
                    edge_.fine_structure_coeff.free is True):
                to_activate_fs.append(edge_)
        self.disable_fine_structure(to_activate_fs)

        # Smart Fitting

        # Without fine structure to determine onset_energy
        edges_to_activate = []
        for edge_ in self._active_edges[edgenumber + 1:]:
            if (edge_.active is True and
                    edge_.onset_energy.value >= nextedgeenergy):
                edge_.active = False
                edges_to_activate.append(edge_)

        self.set_signal_range(start_energy, nextedgeenergy)
        if edge.free_onset_energy is True:
            edge.onset_energy.free = True
            self.fit(**kwargs)
            edge.onset_energy.free = False
            _logger.info("onset_energy = %s", edge.onset_energy.value)
            self._classify_components()
        elif edge.intensity.free is True:
            self.enable_fine_structure(to_activate_fs)
            self.remove_fine_structure_data(to_activate_fs)
            self.disable_fine_structure(to_activate_fs)
            self.fit(**kwargs)

        if len(to_activate_fs) > 0:
            self.set_signal_range(start_energy, nextedgeenergy)
            self.enable_fine_structure(to_activate_fs)
            self.fit(**kwargs)

        self.enable_edges(edges_to_activate)
        # Recover the channel_switches. Remove it or make it smarter.
        self.channel_switches = backup_channel_switches

    def quantify(self):
        """Prints the value of the intensity of all the independent
        active EELS core loss edges defined in the model

        """
        elements = {}
        for edge in self._active_edges:
            if edge.active and edge.intensity.twin is None:
                element = edge.element
                subshell = edge.subshell
                if element not in elements:
                    elements[element] = {}
                elements[element][subshell] = edge.intensity.value
        print()
        print("Absolute quantification:")
        print("Elem.\tIntensity")
        for element in elements:
            if len(elements[element]) == 1:
                for subshell in elements[element]:
                    print("%s\t%f" % (
                        element, elements[element][subshell]))
            else:
                for subshell in elements[element]:
                    print("%s_%s\t%f" % (element, subshell,
                                         elements[element][subshell]))

    def remove_fine_structure_data(self, edges_list=None):
        """Remove the fine structure data from the fitting routine as
        defined in the fine_structure_width parameter of the
        component.EELSCLEdge

        Parameters
        ----------
        edges_list : None or  list of EELSCLEdge or list of edge names
            If None, the operation is performed on all the edges in the model.
            Otherwise, it will be performed only on the listed components.

        See Also
        --------
        enable_edges, disable_edges, enable_background,
        disable_background, enable_fine_structure,
        disable_fine_structure, set_all_edges_intensities_positive,
        unset_all_edges_intensities_positive, enable_free_onset_energy,
        disable_free_onset_energy, fix_edges, free_edges, fix_fine_structure,
        free_fine_structure

        """
        if edges_list is None:
            edges_list = self._active_edges
        else:
            edges_list = [self._get_component(x) for x in edges_list]
        for edge in edges_list:
            if (edge.isbackground is False and
                    edge.fine_structure_active is True):
                start = edge.onset_energy.value
                stop = start + edge.fine_structure_width
                self.remove_signal_range(start, stop)

    def enable_edges(self, edges_list=None):
        """Enable the edges listed in edges_list. If edges_list is
        None (default) all the edges with onset in the spectrum energy
        region will be enabled.

        Parameters
        ----------
        edges_list : None or  list of EELSCLEdge or list of edge names
            If None, the operation is performed on all the edges in the model.
            Otherwise, it will be performed only on the listed components.

        See Also
        --------
        enable_edges, disable_edges, enable_background,
        disable_background, enable_fine_structure,
        disable_fine_structure, set_all_edges_intensities_positive,
        unset_all_edges_intensities_positive, enable_free_onset_energy,
        disable_free_onset_energy, fix_edges, free_edges, fix_fine_structure,
        free_fine_structure

        """

        if edges_list is None:
            edges_list = self.edges
        else:
            edges_list = [self._get_component(x) for x in edges_list]
        for edge in edges_list:
            if edge.isbackground is False:
                edge.active = True
        self.resolve_fine_structure()

    def disable_edges(self, edges_list=None):
        """Disable the edges listed in edges_list. If edges_list is None (default)
        all the edges with onset in the spectrum energy region will be
        disabled.

        Parameters
        ----------
        edges_list : None or  list of EELSCLEdge or list of edge names
            If None, the operation is performed on all the edges in the model.
            Otherwise, it will be performed only on the listed components.

        See Also
        --------
        enable_edges, disable_edges, enable_background,
        disable_background, enable_fine_structure,
        disable_fine_structure, set_all_edges_intensities_positive,
        unset_all_edges_intensities_positive, enable_free_onset_energy,
        disable_free_onset_energy, fix_edges, free_edges, fix_fine_structure,
        free_fine_structure

        """
        if edges_list is None:
            edges_list = self._active_edges
        else:
            edges_list = [self._get_component(x) for x in edges_list]
        for edge in edges_list:
            if edge.isbackground is False:
                edge.active = False
        self.resolve_fine_structure()

    def enable_background(self):
        """Enable the background componets.

        """
        for component in self._background_components:
            component.active = True

    def disable_background(self):
        """Disable the background components.

        """
        for component in self._active_background_components:
            component.active = False

    def enable_fine_structure(self, edges_list=None):
        """Enable the fine structure of the edges listed in edges_list.
        If edges_list is None (default) the fine structure of all the edges
        with onset in the spectrum energy region will be enabled.

        Parameters
        ----------
        edges_list : None or  list of EELSCLEdge or list of edge names
            If None, the operation is performed on all the edges in the model.
            Otherwise, it will be performed only on the listed components.

        See Also
        --------
        enable_edges, disable_edges, enable_background,
        disable_background, enable_fine_structure,
        disable_fine_structure, set_all_edges_intensities_positive,
        unset_all_edges_intensities_positive, enable_free_onset_energy,
        disable_free_onset_energy, fix_edges, free_edges, fix_fine_structure,
        free_fine_structure

        """
        if edges_list is None:
            edges_list = self._active_edges
        else:
            edges_list = [self._get_component(x) for x in edges_list]
        for edge in edges_list:
            if edge.isbackground is False:
                edge.fine_structure_active = True
                edge.fine_structure_coeff.free = True
        self.resolve_fine_structure()

    def disable_fine_structure(self, edges_list=None):
        """Disable the fine structure of the edges listed in edges_list.
        If edges_list is None (default) the fine structure of all the edges
        with onset in the spectrum energy region will be disabled.

        Parameters
        ----------
        edges_list : None or  list of EELSCLEdge or list of edge names
            If None, the operation is performed on all the edges in the model.
            Otherwise, it will be performed only on the listed components.

        See Also
        --------
        enable_edges, disable_edges, enable_background,
        disable_background, enable_fine_structure,
        disable_fine_structure, set_all_edges_intensities_positive,
        unset_all_edges_intensities_positive, enable_free_onset_energy,
        disable_free_onset_energy, fix_edges, free_edges, fix_fine_structure,
        free_fine_structure

        """
        if edges_list is None:
            edges_list = self._active_edges
        else:
            edges_list = [self._get_component(x) for x in edges_list]
        for edge in edges_list:
            if edge.isbackground is False:
                edge.fine_structure_active = False
                edge.fine_structure_coeff.free = False
        self.resolve_fine_structure()

    def set_all_edges_intensities_positive(self):
        for edge in self._active_edges:
            edge.intensity.ext_force_positive = True
            edge.intensity.ext_bounded = True

    def unset_all_edges_intensities_positive(self):
        for edge in self._active_edges:
            edge.intensity.ext_force_positive = False
            edge.intensity.ext_bounded = False

    def enable_free_onset_energy(self, edges_list=None):
        """Enable the automatic freeing of the onset_energy parameter during a
        smart fit for the edges listed in edges_list.
        If edges_list is None (default) the onset_energy of all the edges
        with onset in the spectrum energy region will be freeed.

        Parameters
        ----------
        edges_list : None or  list of EELSCLEdge or list of edge names
            If None, the operation is performed on all the edges in the model.
            Otherwise, it will be performed only on the listed components.

        See Also
        --------
        enable_edges, disable_edges, enable_background,
        disable_background, enable_fine_structure,
        disable_fine_structure, set_all_edges_intensities_positive,
        unset_all_edges_intensities_positive, enable_free_onset_energy,
        disable_free_onset_energy, fix_edges, free_edges, fix_fine_structure,
        free_fine_structure

        """
        if edges_list is None:
            edges_list = self._active_edges
        else:
            edges_list = [self._get_component(x) for x in edges_list]
        for edge in edges_list:
            if edge.isbackground is False:
                edge.free_onset_energy = True

    def disable_free_onset_energy(self, edges_list=None):
        """Disable the automatic freeing of the onset_energy parameter during a
        smart fit for the edges listed in edges_list.
        If edges_list is None (default) the onset_energy of all the edges
        with onset in the spectrum energy region will not be freed.
        Note that if their atribute edge.onset_energy.free is True, the
        parameter will be free during the smart fit.

        Parameters
        ----------
        edges_list : None or  list of EELSCLEdge or list of edge names
            If None, the operation is performed on all the edges in the model.
            Otherwise, it will be performed only on the listed components.

        See Also
        --------
        enable_edges, disable_edges, enable_background,
        disable_background, enable_fine_structure,
        disable_fine_structure, set_all_edges_intensities_positive,
        unset_all_edges_intensities_positive, enable_free_onset_energy,
        disable_free_onset_energy, fix_edges, free_edges, fix_fine_structure,
        free_fine_structure

        """

        if edges_list is None:
            edges_list = self._active_edges
        else:
            edges_list = [self._get_component(x) for x in edges_list]
        for edge in edges_list:
            if edge.isbackground is False:
                edge.free_onset_energy = True

    def fix_edges(self, edges_list=None):
        """Fixes all the parameters of the edges given in edges_list.
        If edges_list is None (default) all the edges will be fixed.

        Parameters
        ----------
        edges_list : None or  list of EELSCLEdge or list of edge names
            If None, the operation is performed on all the edges in the model.
            Otherwise, it will be performed only on the listed components.

        See Also
        --------
        enable_edges, disable_edges, enable_background,
        disable_background, enable_fine_structure,
        disable_fine_structure, set_all_edges_intensities_positive,
        unset_all_edges_intensities_positive, enable_free_onset_energy,
        disable_free_onset_energy, fix_edges, free_edges, fix_fine_structure,
        free_fine_structure

        """
        if edges_list is None:
            edges_list = self._active_edges
        else:
            edges_list = [self._get_component(x) for x in edges_list]
        for edge in edges_list:
            if edge.isbackground is False:
                edge.intensity.free = False
                edge.onset_energy.free = False
                edge.fine_structure_coeff.free = False

    def free_edges(self, edges_list=None):
        """Frees all the parameters of the edges given in edges_list.
        If edges_list is None (default) all the edges will be freeed.

        Parameters
        ----------
        edges_list : None or  list of EELSCLEdge or list of edge names
            If None, the operation is performed on all the edges in the model.
            Otherwise, it will be performed only on the listed components.

        See Also
        --------
        enable_edges, disable_edges, enable_background,
        disable_background, enable_fine_structure,
        disable_fine_structure, set_all_edges_intensities_positive,
        unset_all_edges_intensities_positive, enable_free_onset_energy,
        disable_free_onset_energy, fix_edges, free_edges, fix_fine_structure,
        free_fine_structure

        """

        if edges_list is None:
            edges_list = self._active_edges
        else:
            edges_list = [self._get_component(x) for x in edges_list]
        for edge in edges_list:
            if edge.isbackground is False:
                edge.intensity.free = True

    def fix_fine_structure(self, edges_list=None):
        """Fixes all the parameters of the edges given in edges_list.
        If edges_list is None (default) all the edges will be fixed.

        Parameters
        ----------
        edges_list : None or  list of EELSCLEdge or list of edge names
            If None, the operation is performed on all the edges in the model.
            Otherwise, it will be performed only on the listed components.

        See Also
        --------
        enable_edges, disable_edges, enable_background,
        disable_background, enable_fine_structure,
        disable_fine_structure, set_all_edges_intensities_positive,
        unset_all_edges_intensities_positive, enable_free_onset_energy,
        disable_free_onset_energy, fix_edges, free_edges, fix_fine_structure,
        free_fine_structure

        """
        if edges_list is None:
            edges_list = self._active_edges
        else:
            edges_list = [self._get_component(x) for x in edges_list]
        for edge in edges_list:
            if edge.isbackground is False:
                edge.fine_structure_coeff.free = False

    def free_fine_structure(self, edges_list=None):
        """Frees all the parameters of the edges given in edges_list.
        If edges_list is None (default) all the edges will be freeed.

        Parameters
        ----------
        edges_list : None or  list of EELSCLEdge or list of edge names
            If None, the operation is performed on all the edges in the model.
            Otherwise, it will be performed only on the listed components.

        See Also
        --------
        enable_edges, disable_edges, enable_background,
        disable_background, enable_fine_structure,
        disable_fine_structure, set_all_edges_intensities_positive,
        unset_all_edges_intensities_positive, enable_free_onset_energy,
        disable_free_onset_energy, fix_edges, free_edges, fix_fine_structure,
        free_fine_structure

        """
        if edges_list is None:
            edges_list = self._active_edges
        else:
            edges_list = [self._get_component(x) for x in edges_list]
        for edge in edges_list:
            if edge.isbackground is False:
                edge.fine_structure_coeff.free = True

    def suspend_auto_fine_structure_width(self):
        """Disable the automatic adjustament of the core-loss edges fine
        structure width.

        See Also
        --------
        resume_auto_fine_structure_width

        """
        if self._suspend_auto_fine_structure_width is False:
            self._suspend_auto_fine_structure_width = True
        else:
            warnings.warn("Already suspended, does nothing.")

    def resume_auto_fine_structure_width(self, update=True):
        """Enable the automatic adjustament of the core-loss edges fine
        structure width.

        Parameters
        ----------
        update : bool, optional
            If True, also execute the automatic adjustment (default).


        See Also
        --------
        suspend_auto_fine_structure_width

        """
        if self._suspend_auto_fine_structure_width is True:
            self._suspend_auto_fine_structure_width = False
            if update is True:
                self.resolve_fine_structure()
        else:
            warnings.warn("Not suspended, nothing to resume.")
