/usr/share/pyshared/PyMca/StackSimpleFit.py is in pymca 4.5.0-4.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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# Copyright (C) 2004-2010 European Synchrotron Radiation Facility
#
# This file is part of the PyMCA X-ray Fluorescence Toolkit developed at
# the ESRF by the Beamline Instrumentation Software Support (BLISS) group.
#
# This toolkit is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the Free
# Software Foundation; either version 2 of the License, or (at your option)
# any later version.
#
# PyMCA 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 General Public License for more
# details.
#
# You should have received a copy of the GNU General Public License along with
# PyMCA; if not, write to the Free Software Foundation, Inc., 59 Temple Place,
# Suite 330, Boston, MA 02111-1307, USA.
#
# PyMCA follows the dual licensing model of Trolltech's Qt and Riverbank's PyQt
# and cannot be used as a free plugin for a non-free program.
#
# Please contact the ESRF industrial unit (industry@esrf.fr) if this license
# is a problem for you.
#############################################################################*/
import os
import numpy
try:
from PyMca import ConfigDict
from PyMca import SimpleFitModule
from PyMca import ArraySave
from PyMca import PyMcaDirs
except ImportError:
print("StackSimpleFit is importing from somewhere else")
import ConfigDict
import SimpleFitModule
import ArraySave
import PyMcaDirs
DEBUG = 0
class StackSimpleFit(object):
def __init__(self, fit=None):
if fit is None:
fit = SimpleFitModule.SimpleFit()
self.fit = fit
self.stack_y = None
#self.configuration = None
self.outputDir = PyMcaDirs.outputDir
self.outputFile = None
self.fixedLenghtOutput = True
self.progressCallback = None
self.dataIndex = None
def setProgressCallback(self, method):
"""
The method will be called as method(current_fit_index, total_fit_index)
"""
self.progressCallback = method
def setOutputDirectory(self, outputdir):
self.outputDir = outputdir
def setOutputFileBaseName(self, outputfile):
self.outputFile = outputfile
def setData(self, stack_x, stack_y, sigma=None, xmin=None, xmax=None):
self.stack_x = stack_x
self.stack_y = stack_y
self.stack_sigma = sigma
self.xMin = xmin
self.xMax = xmax
def setDataIndex(self, data_index=None):
self.data_index = data_index
def setConfigurationFile(self, fname):
if not os.path.exists(fname):
raise IOError("File %s does not exist" % fname)
w = ConfigDict.ConfigDict()
w.read(fname)
self.configuration = w
self.setConfiguration(w)
def setConfiguration(self, ddict):
self.configuration = ddict
self.fit.setConfiguration(ddict, try_import=True)
def processStack(self):
data_index = self.dataIndex
if data_index == None:
data_index = -1
if type(data_index) == type(1):
data_index = [data_index]
if len(data_index) > 1:
raise IndexError("Only 1D fitting implemented for the time being")
#this leaves the possibility to fit images by giving
#two indices specifying the image dimensions
self.stackDataIndexList = data_index
stack = self.stack_y
if stack is None:
raise ValueError("No data to be processed")
if hasattr(stack, "info") and hasattr(stack, "data"):
data = stack.data
else:
data = stack
#make sure all the indices are positive
for i in range(len(data_index)):
if data_index[i] < 0:
data_index[i] = range(len(data.shape))[data_index[i]]
#get the total number of fits to be performed
outputDimension = []
nPixels = 1
for i in range(len(data.shape)):
if not (i in data_index):
nPixels *= data.shape[i]
outputDimension.append(data.shape[i])
lenOutput = len(outputDimension)
if lenOutput > 2:
raise ValueError("Rank of output greater than 2")
elif lenOutput == 2:
self._nRows = outputDimension[0]
self._nColumns = outputDimension[1]
else:
self._nRows = outputDimension[0]
self._nColumns = 1
#self.fit.setConfiguration(self.configuration, try_import=True)
self._parameters = None
self._row = 0
self._column = -1
for i in range(nPixels):
if (self._column+1) == self._nColumns:
self._column = 0
self._row += 1
else:
self._column += 1
try:
self.processStackData(i)
except:
print("Error processing index = %d, row = %d column = %d" %\
(i, self._row, self._column))
if DEBUG:
raise
self.onProcessStackFinished()
if self.progressCallback is not None:
self.progressCallback(nPixels, nPixels)
def processStackData(self, i):
self.aboutToGetStackData(i)
x, y, sigma, xmin, xmax = self.getFitInputValues(i)
self.fit.setData(x, y, sigma=sigma, xmin=xmin, xmax=xmax)
self.fit.estimate()
self.estimateFinished()
values, chisq, sigma, niter, lastdeltachi = self.fit.startFit()
self.fitFinished()
def getFitInputValues(self, index):
"""
Returns the fit parameters x, y, sigma, xmin, xmax
"""
row = self._row
column = self._column
data_index = self.stackDataIndexList[0]
#get y
yShape = self.stack_y.shape
if len(yShape) == 3:
if data_index == 0:
y = self.stack_y[:, row, column]
elif data_index == 1:
y = self.stack_y[row, :, column]
else:
y = self.stack_y[row, column]
elif len(yShape) == 2:
if column > 0:
raise ValueError("Column index > 0 on a single column stack")
y = self.stack_y[row]
else:
raise TypeError("Unsupported y data shape lenght")
#get x
if self.stack_x is None:
nValues = y.size
x = numpy.arange(float(nValues))
x.shape = y.shape
self.stack_x = x
xShape = self.stack_x.shape
xSize = self.stack_x.size
sigma = None
if xShape == yShape:
#as many x as y, follow same criterium
if len(xShape) == 3:
if data_index == 0:
x = self.stack_x[:, row, column]
elif data_index == 1:
x = self.stack_x[row, :, column]
else:
x = self.stack_x[row, column]
elif len(xShape) == 2:
if column > 0:
raise ValueError("Column index > 0 on a single column stack")
x = self.stack_x[row]
else:
raise TypeError("Unsupported x data shape lenght")
elif xSize == y.size:
#only one x for all the y values
x = numpy.zeros(y.size, numpy.float)
x[:] = self.stack_x[:]
x.shape = y.shape
else:
raise ValueError("Cannot handle incompatible X and Y values")
if self.stack_sigma is None:
return x, y, sigma, self.xMin, self.xMax
# get sigma
sigmaShape = self.stack_sigma.shape
sigmaSize = self.stack_sigma.size
if sigmaShape == yShape:
#as many sigma as y, follow same criterium
if len(sigmaShape) == 3:
if data_index == 0:
sigma = self.stack_sigma[:, row, column]
elif data_index == 1:
sigma = self.stack_sigma[row, :, column]
else:
sigma = self.stack_sigma[row, column]
elif len(sigmaShape) == 2:
if column > 0:
raise ValueError("Column index > 0 on a single column stack")
sigma = self.stack_sigma[row]
else:
raise TypeError("Unsupported sigma data shape lenght")
elif sigmaSize == y.size:
#only one sigma for all the y values
sigma = numpy.zeros(y.size, numpy.float)
sgima[:] = self.stack_sigma[:]
sigma.shape = y.shape
else:
raise ValueError("Cannot handle incompatible sigma and y values")
return x, y, sigma, self.xMin, self.xMax
def estimateFinished(self):
if DEBUG:
print("Estimate finished")
def aboutToGetStackData(self, idx):
if DEBUG:
print("New spectrum %d" % idx)
self._currentFitIndex = idx
if self.progressCallback is not None:
self.progressCallback(idx, self._nRows * self._nColumns)
if idx == 0:
specfile = os.path.join(self.outputDir,
self.outputFile+".spec")
if os.path.exists(self.outputFile):
os.remove(self.outputFile)
def fitFinished(self):
if DEBUG:
print("fit finished")
#get parameter results
fitOutput = self.fit.getResult(configuration=False)
result = fitOutput['result']
row= self._row
column = self._column
if result is None:
print("result not valid for row %d, column %d" % (row, column))
return
if self.fixedLenghtOutput and (self._parameters is None):
#If it is the first fit, initialize results array
imgdir = os.path.join(self.outputDir, "IMAGES")
if not os.path.exists(imgdir):
os.mkdir(imgdir)
if not os.path.isdir(imgdir):
msg= "%s does not seem to be a valid directory" % imgdir
raise IOError(msg)
self.imgDir = imgdir
self._parameters = []
self._images = {}
self._sigmas = {}
for parameter in result['parameters']:
self._parameters.append(parameter)
self._images[parameter] = numpy.zeros((self._nRows,
self._nColumns),
numpy.float32)
self._sigmas[parameter] = numpy.zeros((self._nRows,
self._nColumns),
numpy.float32)
self._images['chisq'] = numpy.zeros((self._nRows,
self._nColumns),
numpy.float32)
if self.fixedLenghtOutput:
i = 0
for parameter in self._parameters:
self._images[parameter] [row, column] =\
result['fittedvalues'][i]
self._sigmas[parameter] [row, column] =\
result['sigma_values'][i]
i += 1
self._images['chisq'][row, column] = result['chisq']
#specfile output always available
specfile = self.getOutputFileNames()['specfile']
self._appendOneResultToSpecfile(specfile, result=fitOutput)
def _appendOneResultToSpecfile(self, filename, result=None):
if result is None:
result = self.fit.getResult(configuration=False)
scanNumber = self._currentFitIndex
#open file in append mode
fitResult = result['result']
fittedValues = fitResult['fittedvalues']
fittedParameters = fitResult['parameters']
chisq = fitResult['chisq']
text = "\n#S %d %s\n" % (scanNumber, "PyMca Stack Simple Fit")
text += "#N %d\n" % (len(fittedParameters)+2)
text += "#L N Chisq"
for parName in fittedParameters:
text += ' %s' % parName
text += "\n"
text += "1 %f" % chisq
for parValue in fittedValues:
text += "% .7E" % parValue
text += "\n"
sf = open(filename, 'ab')
sf.write(text)
sf.close()
def getOutputFileNames(self):
specfile = os.path.join(self.outputDir,
self.outputFile+".spec")
imgDir = os.path.join(self.outputDir, "IMAGES")
filename = os.path.join(imgDir, self.outputFile)
csv = filename + ".csv"
edf = filename + ".edf"
ddict = {}
ddict['specfile'] = specfile
ddict['csv'] = csv
ddict['edf'] = edf
return ddict
def onProcessStackFinished(self):
if DEBUG:
print("Stack proccessed")
if self.fixedLenghtOutput:
nParameters = len(self._parameters)
datalist = [None] * (2*len(self._sigmas.keys())+1)
labels = []
for i in range(nParameters):
parameter = self._parameters[i]
datalist[2*i] = self._images[parameter]
datalist[2*i + 1] = self._sigmas[parameter]
labels.append(parameter)
labels.append('s(%s)' % parameter)
datalist[-1] = self._images['chisq']
labels.append('chisq')
filenames = self.getOutputFileNames()
csvName = filenames['csv']
edfName = filenames['edf']
ArraySave.save2DArrayListAsASCII(datalist,
csvName,
labels=labels,
csv=True,
csvseparator=";")
ArraySave.save2DArrayListAsEDF(datalist,
edfName,
labels = labels,
dtype=numpy.float32)
def test():
import numpy
import SpecfitFuns
x = numpy.arange(1000.)
data = numpy.zeros((50, 1000), numpy.float)
#the peaks to be fitted
p0 = [100., 300., 50.,
200., 500., 30.,
300., 800., 65]
#generate the data to be fitted
for i in range(data.shape[0]):
nPeaks = 3 - i % 3
data[i,:] = SpecfitFuns.gauss(p0[:3*nPeaks],x)
oldShape = data.shape
data.shape = 1,oldShape[0], oldShape[1]
instance = StackSimpleFit()
instance.setData(x, data)
instance.setConfigurationFile("C:\StackSimpleFit.cfg")
instance.processStack()
if __name__=="__main__":
DEBUG = 0
test()
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