#/*##########################################################################
# Copyright (C) 2004-2022 European Synchrotron Radiation Facility
#
# This file is part of the PyMca X-ray Fluorescence Toolkit developed at
# the ESRF.
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# in the Software without restriction, including without limitation the rights
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# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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__author__ = "V.A. Sole - ESRF"
__contact__ = "sole@esrf.fr"
__license__ = "MIT"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
__doc__ = """
Fast XRF Linear fit of data stack by:

- fixing non-linear parameters to its starting values
- processing the data in chunks instead of point by point

"""
import sys
import os
import numpy
import logging
import traceback
from PyMca5 import StackPluginBase
from PyMca5.PyMcaPhysics import FastXRFLinearFit
from PyMca5.PyMcaPhysics import XRFBatchFitOutput
from PyMca5.PyMcaGui import FastXRFLinearFitWindow
from PyMca5.PyMcaGui import CalculationThread
from PyMca5.PyMcaGui import StackPluginResultsWindow
from PyMca5.PyMcaGui import PyMca_Icons as PyMca_Icons
from PyMca5.PyMcaGui import PyMcaQt as qt

_logger = logging.getLogger(__name__)


class FastXRFLinearFitStackPlugin(StackPluginBase.StackPluginBase):
    """
    Fast XRF Linear fit of data stack by:

    - fixing non-linear parameters to its starting values
    - processing the data in chunks instead of point by point

    """
    def __init__(self, stackWindow, **kw):
        if _logger.getEffectiveLevel() == logging.DEBUG:
            StackPluginBase.pluginBaseLogger.setLevel(logging.DEBUG)
        StackPluginBase.StackPluginBase.__init__(self, stackWindow, **kw)
        self.methodDict = {}
        function = self.calculate
        info = "Fast XRF Linear fit of data stack"
        icon = PyMca_Icons.fit
        self.methodDict["Fit Stack"] =[function,
                                       info,
                                       icon]
        function = self._showWidget
        info = "Show last results"
        icon = PyMca_Icons.brushselect
        self.methodDict["Show"] =[function,
                                  info,
                                  icon]
        self.__methodKeys = ["Fit Stack", "Show"]
        self.configurationWidget = None
        self.fitInstance = None
        self._widget = None
        self.thread = None

    def stackUpdated(self):
        _logger.debug("FastXRFLinearFitStackPlugin.stackUpdated() called")
        self._widget = None

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

    def mySlot(self, ddict):
        _logger.debug("mySlot ", 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'] == "addAllClicked":
            for i in range(len(ddict["images"])):
                self.addImage(ddict['images'][i], ddict['titles'][i])            
        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)

    #Methods implemented by the plugin
    def getMethods(self):
        if self._widget is None:
            return [self.__methodKeys[0]]
        else:
            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]()

    # The specific part
    def calculate(self):
        if self.configurationWidget is None:
            self.configurationWidget = \
                            FastXRFLinearFitWindow.FastXRFLinearFitDialog()
        ret = self.configurationWidget.exec()
        if ret:
            self._executeFunctionAndParameters()

    def _executeFunctionAndParameters(self):
        self._parameters = self.configurationWidget.getParameters()
        self._widget = None
        if self.fitInstance is None:
            self.fitInstance = FastXRFLinearFit.FastXRFLinearFit()
        #self._fitConfigurationFile="E:\DATA\COTTE\CH1777\G4-4720eV-NOWEIGHT-Constant-batch.cfg"

        if _logger.getEffectiveLevel() == logging.DEBUG:
            self.thread = CalculationThread.CalculationThread(\
                            calculation_method=self.actualCalculation)
            self.thread.result = self.actualCalculation()
            self.threadFinished()
        else:
            self.thread = CalculationThread.CalculationThread(\
                            calculation_method=self.actualCalculation)
            self.thread.finished.connect(self.threadFinished)
            self.thread.start()
            message = "Please wait. Calculation going on."
            CalculationThread.waitingMessageDialog(self.thread,
                                parent=self.configurationWidget,
                                message=message)

    def actualCalculation(self):
        activeCurve = self.getActiveCurve()
        if activeCurve is not None:
            x, spectrum, legend, info = activeCurve
        else:
            x = None
            spectrum = None
        if not self.isStackFinite():
            # one has to check for NaNs in the used region(s)
            # for the time being only in the global image
            # spatial_mask = numpy.isfinite(image_data)
            spatial_mask = numpy.isfinite(self.getStackOriginalImage())
            # WDN: any effect?
        stack = self.getStackDataObject()

        fitparams = self._parameters['fit'].copy()
        fitConfigurationFile = fitparams.pop('configuration')
        self.fitInstance.setFitConfigurationFile(fitConfigurationFile)
        if fitparams['weight']:
            # force calculation of the unnormalized sum spectrum
            spectrum = None
        if stack.x in [None, []]:
            x = None
        else:
            x = stack.x[0]

        outparams = self._parameters['output']
        outbuffer = XRFBatchFitOutput.OutputBuffer(**outparams)
        outbuffer = self.fitInstance.fitMultipleSpectra(x=x,
                                                        y=stack,
                                                        ysum=spectrum,
                                                        outbuffer=outbuffer,
                                                        save=False,  # do it later
                                                        **fitparams)
        return outbuffer

    def threadFinished(self):
        try:
            self._threadFinished()
        except:
            msg = qt.QMessageBox()
            msg.setIcon(qt.QMessageBox.Critical)
            msg.setInformativeText(str(sys.exc_info()[1]))
            msg.setDetailedText(traceback.format_exc())
            msg.exec()

    def _threadFinished(self):
        result = self.thread.result
        self.thread = None
        if type(result) == type((1,)):
            #if we receive a tuple there was an error
            if len(result):
                if result[0] == "Exception":
                    # somehow this exception is not caught
                    raise Exception(result[1], result[2])#, result[3])
                    return

        # Show results
        with result.bufferContext(update=True):
            if 'molarconcentrations' in result:
                imageNames = result.labels('parameters', labeltype='title') + \
                             result.labels('molarconcentrations', labeltype='title')
                images = numpy.concatenate((result['parameters'],
                                            result['molarconcentrations']), axis=0)
            elif 'massfractions' in result:
                imageNames = result.labels('parameters', labeltype='title') + \
                             result.labels('massfractions', labeltype='title')
                images = numpy.concatenate((result['parameters'],
                                            result['massfractions']), axis=0)
            else:
                imageNames = result.labels('parameters', labeltype='title')
                images = result['parameters']
            self._widget = StackPluginResultsWindow.StackPluginResultsWindow(\
                                            usetab=False)
            self._widget.buildAndConnectImageButtonBox(replace=True,
                                                       multiple=True)
            qt = StackPluginResultsWindow.qt
            self._widget.sigMaskImageWidgetSignal.connect(self.mySlot)
            self._widget.setStackPluginResults(images,
                                               image_names=imageNames)
            self._showWidget()

            # Save results
            result.save()

    def _showWidget(self):
        if self._widget is None:
            return
        #Show
        self._widget.show()
        self._widget.raise_()

        #update
        self.selectionMaskUpdated()

MENU_TEXT = "Fast XRF Linear Fit"
def getStackPluginInstance(stackWindow, **kw):
    ob = FastXRFLinearFitStackPlugin(stackWindow)
    return ob
