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#/*##########################################################################
#
# The PyMca X-Ray Fluorescence Toolkit
#
# Copyright (c) 2004-2016 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"
import copy
from . import Elements
from . import ConcentrationsTool
DEBUG = 0
class SingleLayerStrategy(object):
def __init__(self):
self._tool = ConcentrationsTool.ConcentrationsTool()
def applyStrategy(self, fitResult, fluorescenceRates, currentIteration=None):
"""
Provided a fit result, it returns an new fit configuration and
a positive integer to indicate the strategy procedure has not finished.
Returning an empty fit configuration, or a number of iterations equal 0
will indicate the process is over.
"""
if DEBUG:
print("SingleLayerStrategy called with iteration ", currentIteration)
newConfiguration = copy.deepcopy(fitResult['config'])
strategyConfiguration = newConfiguration['SingleLayerStrategy']
if currentIteration is None:
currentIteration = strategyConfiguration['iterations']
if currentIteration < 1:
# enough for the journey
return {}, 0
# calculate concentrations with current configuration
ddict = {}
ddict.update(newConfiguration['concentrations'])
ddict, addInfo = self._tool.processFitResult( \
config=ddict,
fitresult={"result":fitResult},
elementsfrommatrix=False,
fluorates = fluorescenceRates,
addinfo=True)
# find the layer to be updated the matrix
matrixKey = None
for attenuator in list(newConfiguration['attenuators'].keys()):
if not newConfiguration['attenuators'][attenuator][0]:
continue
if attenuator.upper() == "MATRIX":
if newConfiguration['attenuators'][attenuator][1].upper() != \
"MULTILAYER":
matrixKey = attenuator
else:
matrixKey = "MULTILAYER"
if matrixKey:
break
if matrixKey != "MULTILAYER":
parentKey = 'attenuators'
daughterKey = matrixKey
else:
parentKey = "multilayer"
daughterKey = None
if newConfiguration["SingleLayerStrategy"]["layer"].upper() == \
["AUTO"]:
# we have to find the layer where we should work
firstLayer = None
for layer in newConfiguration[parentKey]:
if newConfiguration[parentKey][layer][0]:
material = newConfiguration[parentKey][layer][1]
composition = Elements.getMaterialMassFractions( \
[material], [1.0])
for group in strategyConfiguration["peaks"]:
if "-" in group:
continue
ele = group.split()[0]
if ele in composition:
daughterLayer = layer
break
if firstLayer is None:
firstLayer = layer
if daughterKey is None:
daughterKey = firstLayer
else:
daughterKey = newConfiguration["SingleLayerStrategy"]["layer"]
if daughterKey is None:
raise ValueError("Cannot find appropriate sample layer")
# newConfiguration[parentKey][daughterKey] composition is to be updated
# get the new composition
total = 0.0
CompoundList = []
CompoundFraction = []
materialCounter = -1
for group in strategyConfiguration["peaks"]:
materialCounter += 1
if "-" in group:
continue
ele = group.split()[0]
material = strategyConfiguration["materials"][materialCounter]
if material in ["-", ele, ele + "1"]:
CompoundList.append(ele)
CompoundFraction.append(\
ddict["mass fraction"][group])
else:
massFractions = Elements.getMaterialMassFractions( \
[material], [1.0])
CompoundFraction.append(massFractions[ele] / \
ddict["mass fraction"][group])
CompoundList.append(material)
total += CompoundFraction[-1]
if strategyConfiguration["completer"] not in ["-"]:
if total < 1.0:
CompoundList.append(strategyConfiguration["completer"])
CompoundFraction.append(1.0 - total)
else:
for i in range(len(CompoundFraction)):
CompoundFraction[i] /= total;
else:
for i in range(len(CompoundFraction)):
CompoundFraction[i] /= total;
materialName = "SingleLayerStrategyMaterial"
newConfiguration["materials"][materialName] = \
{"Density": newConfiguration['attenuators'][attenuator][2],
"Thickness":newConfiguration['attenuators'][attenuator][3],
"CompoundList":CompoundList,
"CompoundFraction":CompoundFraction,
"Comment":"Last Single Layer Strategy iteration"}
# and update it
newConfiguration[parentKey][daughterKey][1] = materialName
if DEBUG:
print("Updated sample material: ", \
newConfiguration["materials"][materialName])
return newConfiguration, currentIteration - 1
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