#/*########################################################################## # Copyright (C) 2004-2021 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" __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 from PyMca5 import StackPluginBase from PyMca5.PyMcaGui import CalculationThread from PyMca5.PyMcaGui.math.NNMAWindow import NNMAParametersDialog from PyMca5.PyMcaGui import StackPluginResultsWindow from PyMca5.PyMcaGui import PyMca_Icons qt = StackPluginResultsWindow.qt _logger = logging.getLogger(__name__) class NNMAStackPlugin(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 = {'Calculate': [self.calculate, "Perform NNMA", None], 'Show': [self._showWidget, "Show last results", PyMca_Icons.brushselect]} self.__methodKeys = ['Calculate', 'Show'] self.configurationWidget = None self.widget = None self.thread = None def stackUpdated(self): _logger.debug("NNMAStackPlugin.stackUpdated() called") self.configurationWidget = None 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 %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'] == "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): stack = self.getStackDataObject() mcaIndex = stack.info.get('McaIndex') shape = stack.data.shape stack = None if mcaIndex not in [-1, len(shape) - 1]: raise IndexError("NNMA does not support stacks of images yet") return if self.configurationWidget is None: self.configurationWidget = NNMAParametersDialog(None, regions=True) self._status = qt.QLabel(self.configurationWidget) self._status.setAlignment(qt.Qt.AlignHCenter) font = qt.QFont(self._status.font()) font.setBold(True) self._status.setFont(font) self._status.setText("Ready") self.configurationWidget.layout().addWidget(self._status) activeCurve = self.getActiveCurve() if activeCurve is None: #I could get some defaults from the stack itslef raise ValueError("Please select an active curve") return x, spectrum, legend, info = activeCurve spectrumLength = int(max(spectrum.shape)) oldValue = self.configurationWidget.nPC.value() self.configurationWidget.nPC.setMaximum(spectrumLength) self.configurationWidget.nPC.setValue(min(oldValue, spectrumLength)) binningOptions = [1] for number in [2, 3, 4, 5, 7, 9, 10, 11, 13, 15, 17, 19]: if (spectrumLength % number) == 0: binningOptions.append(number) # TODO: Should inform the configuration widget about the possibility # to encounter non-finite data? ddict = {'options': binningOptions, 'binning': 1, 'method': 0} self.configurationWidget.setParameters(ddict) y = spectrum self.configurationWidget.setSpectrum(x, y, legend=legend, info=info) self.configurationWidget.show() self.configurationWidget.raise_() ret = self.configurationWidget.exec() if ret: self._executeFunctionAndParameters() def _executeFunctionAndParameters(self): _logger.debug("NNMAStackPlugin _executeFunctionAndParameters") self.widget = None self.thread = CalculationThread.CalculationThread(\ calculation_method=self.actualCalculation) self.thread.finished.connect(self.threadFinished) self.configurationWidget.show() message = "Please wait. NNMA Calculation going on." _logger.debug("NNMAStackPlugin starting thread") self.thread.start() _logger.debug("NNMAStackPlugin waitingMessageDialog") CalculationThread.waitingMessageDialog(self.thread, message=message, parent=self.configurationWidget) _logger.debug("NNMAStackPlugin waitingMessageDialog passed") def actualCalculation(self): _logger.debug("NNMAStackPlugin actualCalculation") nnmaParameters = self.configurationWidget.getParameters() self._status.setText("Calculation going on") self.configurationWidget.setEnabled(False) #self.configurationWidget.close() #At some point I should make sure I get directly the #function and the parameters from the configuration widget function = nnmaParameters['function'] ddict = {} ddict.update(nnmaParameters['kw']) ddict['ncomponents'] = nnmaParameters['npc'] ddict['binning'] = nnmaParameters['binning'] ddict['spectral_mask'] = nnmaParameters['spectral_mask'] #ddict['kmeans'] = False 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()) ddict['mask'] = spatial_mask del nnmaParameters stack = self.getStackDataObject() if isinstance(stack, numpy.ndarray): if stack.data.dtype not in [numpy.float64, numpy.float32]: _logger.warning("WARNING: Non floating point data") text = "Calculation going on." text += " WARNING: Non floating point data." self._status.setText(text) oldShape = stack.data.shape result = function(stack, **ddict) if stack.data.shape != oldShape: stack.data.shape = oldShape return result def threadFinished(self): _logger.debug("NNMAStackPlugin threadFinished") 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 type(result[0]) == type("Exception"): if result[0] == "Exception": self._status.setText("Ready after calculation error") self.configurationWidget.setEnabled(True) raise Exception(result[1], result[2]) return self._status.setText("Ready") curve = self.configurationWidget.getSpectrum(binned=True) if curve not in [None, []]: xValues = curve[0] else: xValues = None self.configurationWidget.setEnabled(True) self.configurationWidget.close() images, eigenValues, eigenVectors = result imageNames = None vectorNames = None nimages = images.shape[0] imageNames = [] vectorNames = [] vectorTitles = [] for i in range(nimages): imageNames.append("NNMA Image %02d" % i) vectorNames.append("NNMA Component %02d" % i) vectorTitles.append("%g %% explained intensity" %\ eigenValues[i]) _logger.debug("NNMAStackPlugin threadFinished. Create widget") self.widget = StackPluginResultsWindow.StackPluginResultsWindow(\ usetab=True) _logger.debug("NNMAStackPlugin threadFinished. Widget created") self.widget.buildAndConnectImageButtonBox(replace=True, multiple=True) qt = StackPluginResultsWindow.qt self.widget.sigMaskImageWidgetSignal.connect(self.mySlot) if xValues is not None: xValues = [xValues] * nimages self.widget.setStackPluginResults(images, spectra=eigenVectors, image_names=imageNames, xvalues=xValues, spectra_names=vectorNames, spectra_titles=vectorTitles) self._showWidget() def _showWidget(self): if self.widget is None: return #Show self.widget.show() self.widget.raise_() #update self.selectionMaskUpdated() MENU_TEXT = "PyMca NNMA" def getStackPluginInstance(stackWindow, **kw): ob = NNMAStackPlugin(stackWindow) return ob