#/*##########################################################################
# Copyright (C) 2004-2014 V.A. Sole, European Synchrotron Radiation Facility
#
# This file is part of the PyMca X-ray Fluorescence Toolkit developed at
# the ESRF by the Software group.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
#############################################################################*/
__author__ = "V.A. Sole - ESRF Data Analysis"
__contact__ = "sole@esrf.fr"
__license__ = "MIT"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
"""

A Stack plugin is a module that will be automatically added to the PyMca stack windows
in order to perform user defined operations on the data stack.

These plugins will be compatible with any stack window that provides the functions:
    #data related
    getStackDataObject
    getStackData
    getStackInfo
    setStack

    #images related
    addImage
    removeImage
    replaceImage

    #mask related
    setSelectionMask
    getSelectionMask

    #displayed curves
    getActiveCurve
    getGraphXLimits
    getGraphYLimits

    #information method
    stackUpdated
    selectionMaskUpdated
"""
import numpy
import logging

_logger = logging.getLogger(__name__)

try:
    from PyMca5 import StackPluginBase
    from PyMca5.PyMcaGui import CalculationThread
except ImportError:
    _logger.warning("XASStackNormalizationPlugin importing bases from somewhere else")
    from . import StackPluginBase
    from . import CalculationThread

from PyMca5.PyMcaGui import PyMcaQt as qt
from PyMca5.PyMcaGui import StackPluginResultsWindow
from PyMca5.PyMcaPhysics.xas import XASNormalization
from PyMca5.PyMcaGui.physics.xas import XASNormalizationWindow


class XASStackNormalizationPlugin(StackPluginBase.StackPluginBase):
    def __init__(self, stackWindow, **kw):
        if _logger.getEffectiveLevel() == logging.DEBUG:
            StackPluginBase.pluginBaseLogger.setLevel(logging.DEBUG)
        StackPluginBase.StackPluginBase.__init__(self, stackWindow, **kw)
        self.methodDict = {}
        text = "Replace current stack by a normalized one."
        function = self.XASNormalize
        info = text
        icon = None
        self.methodDict["XANES Normalization"] =[function,
                                                 info,
                                                 icon]

        self.__methodKeys = ["XANES Normalization"]
        self.widget = None
        self.imageWidget = None

    #Methods implemented by the plugin
    def stackUpdated(self):
        if self.widget is not None:
            self.widget.close()
        self.widget = None

    def selectionMaskUpdated(self):
        if self.imageWidget is None:
            return
        if self.imageWidget.isHidden():
            return
        mask = self.getStackSelectionMask()
        self.imageWidget.setSelectionMask(mask)

    def stackClosed(self):
        if self.imageWidget is not None:
            self.imageWidget.close()
        if self.widget is not None:
            self.widget.close()

    def getMethods(self):
        return self.__methodKeys

    def getMethodToolTip(self, name):
        return self.methodDict[name][1]

    def getMethodPixmap(self, name):
        return self.methodDict[name][2]

    def applyMethod(self, name):
        return self.methodDict[name][0]()


    # own stuff
    def mySlot(self, ddict):
        _logger.debug("mySlot %s %s", ddict['event'], ddict.keys())
        if ddict['event'] == "selectionMaskChanged":
            self.setStackSelectionMask(ddict['current'])
        elif ddict['event'] == "addImageClicked":
            self.addImage(ddict['image'], ddict['title'])
        elif ddict['event'] == "removeImageClicked":
            self.removeImage(ddict['title'])
        elif ddict['event'] == "replaceImageClicked":
            self.replaceImage(ddict['image'], ddict['title'])
        elif ddict['event'] == "resetSelection":
            self.setStackSelectionMask(None)

    def XASNormalize(self):
        stack = self.getStackDataObject()
        if not isinstance(stack.data, numpy.ndarray):
            text = "This method does not work with dynamically loaded stacks"
            raise TypeError(text)
        activeCurve = self.getActiveCurve()
        if activeCurve in [None, []]:
            return
        x, spectrum, legend, info = activeCurve
        if self.widget is None:
            self.widget = XASNormalizationWindow.XASNormalizationDialog(None,
                                                spectrum, energy=x)
        else:
            oldParameters = self.widget.getParameters()
            oldEnergy = self.widget.parametersWidget.energy
            oldEMin = oldEnergy.min()
            oldEMax = oldEnergy.max()
            self.widget.setData(spectrum, energy=x)
            if abs(oldEMin - x.min()) < 1:
                if abs(oldEMax - x.max()) < 1:
                    self.widget.setParameters(oldParameters)
        ret = self.widget.exec()
        if ret:
            parameters = self.widget.getParameters()
            # TODO: this dictionary adaptation should be made
            #       by the configuration
            if parameters['auto_edge']:
                edge = None
            else:
                edge = parameters['edge_energy']
            energy = x
            pre_edge_regions = parameters['pre_edge']['regions']
            post_edge_regions = parameters['post_edge']['regions']
            algorithm ='polynomial'
            algorithm_parameters = {}
            algorithm_parameters['pre_edge_order'] = parameters['pre_edge']\
                                                             ['polynomial']
            algorithm_parameters['post_edge_order'] = parameters['post_edge']\
                                                             ['polynomial']

            result  = self.__replaceStackByXASNormalizedData(stack,
                                            energy=energy,
                                            edge=edge,
                                            pre_edge_regions=pre_edge_regions,
                                            post_edge_regions=post_edge_regions,
                                            algorithm=algorithm,
                                            algorithm_parameters=algorithm_parameters)
            if result[0] == 'Exception':
                # exception occurred
                raise Exception(result[1], result[2], result[3])
            else:
                edges, jumps, errors = result
            images, names = self.getStackROIImagesAndNames()
            edges.shape = images[0].shape
            jumps.shape = images[0].shape
            errors.shape = images[0].shape
            self.setStack(stack)
            if self.imageWidget is None:
                self.imageWidget = StackPluginResultsWindow.StackPluginResultsWindow(\
                                        usetab=False,profileselection=True)
                self.imageWidget.buildAndConnectImageButtonBox()
                qt = StackPluginResultsWindow.qt
                self.imageWidget.sigMaskImageWidgetSignal.connect(self.mySlot)
                self.methodDict["Show Images"] =[self._showImageWidget,
                                                 "Show calculated jump and edge position images",
                                                 None]
                self.__methodKeys.append("Show Images")
                self.imageWidget.setStackPluginResults([jumps, errors, edges],
                                                        image_names=['Jump',
                                                                     'Errors',
                                                                     'Edge Position'])
            self._showImageWidget()

    def __replaceStackByXASNormalizedData(self, *var, **kw):
        self._progress = 0.0
        thread = CalculationThread.CalculationThread(\
                calculation_method=self.replaceStackByXASNormalizedData,
                calculation_vars=var,
                calculation_kw=kw)
        thread.start()
        CalculationThread.waitingMessageDialog(thread,
                                               message="Please wait. Calculation going on.",
                                               parent=self.widget,
                                               modal=True,
                                               update_callback=self._waitingCallback)
        return thread.result

    def _waitingCallback(self):
        ddict = {}
        ddict['message'] = "Calculation Progress = %d %%" % self._progress
        return ddict

    def _showImageWidget(self):
        if self.imageWidget is None:
            return
        #Show
        self.imageWidget.show()
        self.imageWidget.raise_()

        #update
        self.selectionMaskUpdated()

    def replaceStackByXASNormalizedData(self,
                                        stack,
                                        energy=None,
                                        edge=None,
                                        pre_edge_regions=None,
                                        post_edge_regions=None,
                                        algorithm='polynomial',
                                        algorithm_parameters=None):
        """
        Performs an in place replacement of a set of spectra by a set of
        normalized spectra.
        """
        mcaIndex = -1
        if hasattr(stack, "info") and hasattr(stack, "data"):
            actualData = stack.data
            mcaIndex = stack.info.get('McaIndex', -1)
        else:
            actualData = stack
        if not isinstance(actualData, numpy.ndarray):
            raise TypeError("Currently this method only supports numpy arrays")

        # Take a data view
        oldShape = actualData.shape
        data = actualData[:]
        DONE = 0
        if mcaIndex in [-1, len(data.shape)-1]:
            data.shape = -1, oldShape[-1]
            edges = numpy.zeros(data.shape[0], numpy.float32)
            jumps = numpy.zeros(data.shape[0], numpy.float32)
            errors = numpy.zeros(data.shape[0], numpy.float32)
            total = 0.01 * data.shape[0]
            for i in range(data.shape[0]):
                self._progress = i / total
                try:
                    ene, spe, ed, jmp = XASNormalization.XASNormalization(data[i,:],
                                energy=energy,
                                edge=edge,
                                pre_edge_regions=pre_edge_regions,
                                post_edge_regions=post_edge_regions,
                                algorithm=algorithm,
                                algorithm_parameters=algorithm_parameters)[0:4]
                except:
                    # what to do?
                    # for the data is clear (set to 0)
                    # for the jump 0 is also a good compromise
                    # for the edge?
                    data[i, :] = 0
                    errors[i] = 1
                    jumps[i] = 0
                    edges[i] = 0
                    continue
                if not DONE:
                    c0 = (numpy.nonzero(energy >= (ed + pre_edge_regions[0][0]))[0]).min()
                    c1 = (numpy.nonzero(energy <= (ed + post_edge_regions[-1][1]))[-1]).max()
                    c1 += 1
                    DONE = True
                if ((spe.max()-spe.min()) > 10.) or (jmp < 0):
                    data[i, :] = 0.0
                    # should I give some useless values?
                    edges[i] = 0.0
                    # perhaps the case of large jump should be kept ...
                    jumps[i] = 0.0
                elif 0:
                    # this approach removed
                    data[i,:c0] = spe[c0]
                    data[i, c0:c1] = spe[c0:c1]
                    if c1 < data.shape[1]:
                        data[i, c1:] = spe[c1]
                    edges[i] = ed
                    jumps[i] = jmp
                else:
                    # it seems more appropriate to set the channels below and
                    # above limits to 0 than to the corresponding limits of the region
                    data[i,:c0] = 0.0
                    data[i, c0:c1] = spe[c0:c1]
                    data[i, c1:] = 0.0
                    edges[i] = ed
                    jumps[i] = jmp
            self._progress = 100
            data.shape = oldShape
        elif mcaIndex == 0:
            data.shape = oldShape[0], -1
            edges = numpy.zeros(data.shape[-1], numpy.float32)
            jumps = numpy.zeros(data.shape[-1], numpy.float32)
            errors = numpy.zeros(data.shape[-1], numpy.float32)
            total = 0.01 * data.shape[-1]
            for i in range(data.shape[-1]):
                self._progress = i / total
                try:
                    ene, spe, ed, jmp = XASNormalization.XASNormalization(data[:, i],
                              energy=energy,
                              edge=edge,
                              pre_edge_regions=pre_edge_regions,
                              post_edge_regions=post_edge_regions,
                              algorithm=algorithm,
                              algorithm_parameters=algorithm_parameters)[0:4]
                except:
                    # what to do?
                    # for the data is clear (set to 0)
                    # for the jump 0 is also a good compromise
                    # for the edge?
                    data[:, i] = 0
                    jumps[i] = 0
                    edges[i] = 0
                    errors[i] = 1
                    continue
                if not DONE:
                    c0 = (numpy.nonzero(energy >= (ed + pre_edge_regions[0][0]))[0]).min()
                    c1 = (numpy.nonzero(energy <= (ed + post_edge_regions[-1][1]))[-1]).max()
                    c1 += 1
                    DONE = True
                if ((spe.max()-spe.min()) > 10.) or (jmp < 0):
                    data[:, i] = 0.0
                    # should I give some useless values?
                    edges[i] = 0.0
                    jumps[i] = 0.0
                else:
                    # it seems more appropriate to set the channels below and
                    # above limits to 0 than to the corresponding limits of the region
                    data[:c0, i] = 0.0
                    data[c0:c1, i] = spe[c0:c1]
                    if c1 < data.shape[0]:
                        data[c1:, i] = 0.0
                    edges[i] = ed
                    jumps[i] = jmp
            self._progress = 100
            data.shape = oldShape
        else:
            raise ValueError("Unsupported 1D index %d" % mcaIndex)
        return edges, jumps, errors


MENU_TEXT = "XAS Stack Normalization"
def getStackPluginInstance(stackWindow, **kw):
    ob = XASStackNormalizationPlugin(stackWindow)
    return ob
