# -*- coding: utf-8 -*- #/*########################################################################## # Copyright (C) 2004-2013 European Synchrotron Radiation Facility # # This file is part of the PyMca X-ray Fluorescence Toolkit developed at # the ESRF by the Software group. # # This file is free software; you can redistribute it and/or modify it # under the terms of the GNU Lesser General Public License as published by the # Free Software Foundation; either version 2 of the License, or (at your option) # any later version. # # This file 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 Lesser General Public License for more # details. # #############################################################################*/ __author__ = "V.A. Sole - ESRF Software Group" """ 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 try: from PyMca import StackPluginBase from PyMca import CalculationThread except ImportError: print("XASStackNormalizationPlugin importing bases from somewhere else") from . import StackPluginBase from . import CalculationThread try: from PyMca import PyMcaQt as qt from PyMca import StackPluginResultsWindow from PyMca import XASNormalization from PyMca import XASNormalizationWindow except ImportError: print("XASStackNormalizationPlugin importing from somewhere else") import PyMcaQt as qt import StackPluginResultsWindow import XASNormalization import XASNormalizationWindow DEBUG = 0 class XASStackNormalizationPlugin(StackPluginBase.StackPluginBase): def __init__(self, stackWindow, **kw): StackPluginBase.DEBUG = 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 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): if DEBUG: print("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'] == "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 is 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 qt.QObject.connect(self.imageWidget, qt.SIGNAL('MaskImageWidgetSignal'), 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