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#/*##########################################################################
#
# The PyMca X-Ray Fluorescence Toolkit
#
# Copyright (c) 2004-2014 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"
__doc__ = """
This module implements several mathematical functions:
- peak search
- center of mass search
- fwhm search
WARNING : array are numpy.ndarray objects.
"""
import numpy
def search_peak(xdata, ydata):
"""
Search a peak and its position in arrays xdata ad ydata.
Return three integer:
- peak position
- peak value
- index of peak position in array xdata
This result may accelerate the fwhm search.
"""
ymax = max(ydata)
idx = __give_index(ymax,ydata)
return xdata[idx],ymax,idx
def search_com(xdata,ydata):
"""
Return the center of mass in arrays xdata and ydata
"""
# make sure com is inside the region
ydata = ydata - numpy.min(ydata)
num = numpy.sum(xdata*ydata)
denom = numpy.sum(ydata).astype(numpy.float64)
try:
result = num/denom
except ZeroDivisionError:
result = numpy.mean(x)
return result
def search_fwhm(xdata,ydata,peak=None,index=None):
"""
Search a fwhm and its center in arrays xdata and ydatas.
If no fwhm is found, (0,0) is returned.
peak and index which are coming from search_peak result, may
accelerate calculation
"""
if peak is None or index is None:
x,mypeak,index_peak = search_peak(xdata,ydata)
else:
mypeak = peak
index_peak = index
mymin = numpy.min(ydata)
hm = (mypeak-mymin)/2 + mymin
idx = index_peak
lpeak = False
upeak = False
if numpy.any(ydata[0:idx] < hm):
lpeak = True
if numpy.any(ydata[idx:] < hm):
upeak = True
if lpeak and upeak:
# it is a peak so we keep the data and half-max
pass
elif lpeak:
# it is step-like data with a positive gradient
ydata = numpy.gradient(ydata)
hm = (numpy.max(ydata)-numpy.min(ydata))/2+numpy.min(ydata)
else:
# it is step-like data with a negative gradient
ydata = -1*numpy.gradient(ydata)
hm = (numpy.max(ydata)-numpy.min(ydata))/2+numpy.min(ydata)
index_peak = numpy.argmax(ydata)
idx = index_peak
try:
while ydata[idx] >= hm:
idx = idx-1
x0 = xdata[idx]
x1 = xdata[idx+1]
y0 = ydata[idx]
y1 = ydata[idx+1]
lhmx = (hm*(x1-x0) - (y0*x1)+(y1*x0)) / (y1-y0)
except ZeroDivisionError:
lhmx = 0
except IndexError:
lhmx = xdata[0]
idx = index_peak
try:
while ydata[idx] >= hm:
idx = idx+1
x0 = xdata[idx-1]
x1 = xdata[idx]
y0 = ydata[idx-1]
y1 = ydata[idx]
uhmx = (hm*(x1-x0) - (y0*x1)+(y1*x0)) / (y1-y0)
except ZeroDivisionError:
uhmx = 0
except IndexError:
uhmx = xdata[-1]
FWHM = uhmx - lhmx
CFWHM = (uhmx+lhmx)/2
return FWHM,CFWHM
def __give_index(elem,array):
"""
Return the index of elem in array
"""
mylist = array.tolist()
return mylist.index(elem)
def test():
pass
if __name__ == '__main__':
test()
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