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#/*##########################################################################
#
# The PyMca X-Ray Fluorescence Toolkit
#
# Copyright (c) 2004-2015 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 numpy
import logging
from . import SGModule
_logger = logging.getLogger(__name__)
class SimpleMath(object):
def derivate(self,xdata,ydata, xlimits=None):
x=numpy.array(xdata, copy=False, dtype=numpy.float)
y=numpy.array(ydata, copy=False, dtype=numpy.float)
if xlimits is not None:
i1=numpy.nonzero((xdata>=xlimits[0])&\
(xdata<=xlimits[1]))[0]
x=numpy.take(x,i1)
y=numpy.take(y,i1)
i1 = numpy.argsort(x)
x=numpy.take(x,i1)
y=numpy.take(y,i1)
deltax=x[1:] - x[:-1]
i1=numpy.nonzero(abs(deltax)>0.0000001)[0]
x=numpy.take(x, i1)
y=numpy.take(y, i1)
minDelta = deltax[deltax > 0]
if minDelta.size:
minDelta = minDelta.min()
else:
# all points are equal
minDelta = 1.0
xInter = numpy.arange(x[0]-minDelta,x[-1]+minDelta,minDelta)
yInter = numpy.interp(xInter, x, y, left=y[0], right=y[-1])
if len(yInter) > 499:
npoints = 5
else:
npoints = 3
degree = 1
order = 1
coeff = SGModule.calc_coeff(npoints, degree, order)
N = int(numpy.size(coeff-1)/2)
yInterPrime = numpy.convolve(yInter, coeff, mode='valid')/minDelta
i1 = numpy.nonzero((x>=xInter[N+1]) & (x <= xInter[-N]))[0]
x = numpy.take(x, i1)
result = numpy.interp(x, xInter[(N+1):-N],
yInterPrime[1:],
left=yInterPrime[1],
right=yInterPrime[-1])
return x, result
def average(self, xarr, yarr, x=None):
"""
:param xarr : List containing x values in 1-D numpy arrays
:param yarr : List containing y Values in 1-D numpy arrays
:param x: x values of the final average spectrum (or None)
:return: Average spectrum. In case of invalid input (None, None) tuple is returned.
From the spectra given in xarr & yarr, the method determines the overlap in
the x-range. For spectra with unequal x-ranges, the method interpolates all
spectra on the values given in x if provided or the first curve and averages them.
"""
if (len(xarr) != len(yarr)) or\
(len(xarr) == 0) or (len(yarr) == 0):
_logger.debug('specAverage -- invalid input!\n'
'Array lengths do not match or are 0')
return None, None
same = True
if x == None:
SUPPLIED = False
x0 = xarr[0]
else:
SUPPLIED = True
x0 = x
for x in xarr:
if len(x0) == len(x):
if numpy.all(x0 == x):
pass
else:
same = False
break
else:
same = False
break
xsort = []
ysort = []
for (x,y) in zip(xarr, yarr):
if numpy.all(numpy.diff(x) > 0.):
# All values sorted
xsort.append(x)
ysort.append(y)
else:
# Sort values
mask = numpy.argsort(x)
xsort.append(x.take(mask))
ysort.append(y.take(mask))
if SUPPLIED:
xmin0 = x0.min()
xmax0 = x0.max()
else:
xmin0 = xsort[0][0]
xmax0 = xsort[0][-1]
if (not same) or (not SUPPLIED):
# Determine global xmin0 & xmax0
for x in xsort:
xmin = x.min()
xmax = x.max()
if xmin > xmin0:
xmin0 = xmin
if xmax < xmax0:
xmax0 = xmax
if xmax <= xmin:
_logger.debug('specAverage -- \n'
'No overlap between spectra!')
return numpy.array([]), numpy.array([])
# make sure x0 is sorted
mask = numpy.argsort(x0)
x0 = numpy.take(x0, mask)
# Clip xRange to maximal overlap in spectra
mask = numpy.nonzero((x0 >= xmin0) &
(x0 <= xmax0))[0]
xnew = numpy.take(x0, mask)
ynew = numpy.zeros(len(xnew))
# Perform average
for (x, y) in zip(xsort, ysort):
if same:
ynew += y
else:
yinter = numpy.interp(xnew, x, y)
ynew += numpy.asarray(yinter)
num = len(yarr)
ynew /= num
idx = numpy.isfinite(ynew)
return xnew[idx], ynew[idx]
def smooth(self, *var, **kw):
"""
smooth(self,*vars,**kw)
Usage: self.smooth(y)
self.smooth(y=y)
self.smooth()
"""
if 'y' in kw:
ydata=kw['y']
elif len(var) > 0:
ydata=var[0]
else:
ydata=self.y
f=[0.25,0.5,0.25]
result=numpy.array(ydata, copy=False, dtype=numpy.float)
if len(result) > 1:
result[1:-1]=numpy.convolve(result,f,mode=0)
result[0]=0.5*(result[0]+result[1])
result[-1]=0.5*(result[-1]+result[-2])
return result
if __name__ == "__main__":
x = numpy.arange(100.)*0.25
y = x*x + 2 * x
a = SimpleMath()
#print(a.average(x,y))
xplot, yprime = a.derivate(x, y)
print("Found:")
for i in range(0,10):
print("x = %f y'= %f expected = %f" % (xplot[i], yprime[i], 2*xplot[i]+2))
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