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#/*##########################################################################
# Copyright (C) 2004-2022 European Synchrotron Radiation Facility
#
# This file is part of the PyMca X-ray Fluorescence Toolkit developed at
# the ESRF.
#
# 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"
__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
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