#/*########################################################################## # 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