def updateGraph(self, ddict):
points = ddict['points']
degree = ddict['degree']
order = ddict['order']
self.background = SGModule.getSavitzkyGolay(self.spectrum,
points,
degree=degree,
order=order)
# if the x are decreasing the result is not correct
if order % 2:
if self.xValues is not None:
if self.xValues[0] > self.xValues[-1]:
self.background *= -1
if order > 0:
maptoy2 = "right"
else:
maptoy2 = "left"
self.graph.addCurve(self.xValues,
self.background,
"Filtered Spectrum",
replace=False,
yaxis=maptoy2)
#Force information update
legend = self.graph.getActiveCurve(just_legend=True)
if legend.startswith('Filtered'):
self.graph.setActiveCurve(legend)
def _pymca_SG(ydat, npoints=3, degree=1, order=0):
"""call to symmetric Savitzky-Golay filter in PyMca
Parameters
----------
ydat : 1D array contaning the data to smooth
npoints : integer [3], means that 2*npoints+1 values contribute
to the smoother.
degree : degree of fitting polynomial
order : is degree of implicit differentiation
0 means that filter results in smoothing of function
1 means that filter results in smoothing the first
derivative of function.
and so on ...
Returns
-------
ys : smoothed array
"""
if HAS_SGMODULE:
return SGModule.getSavitzkyGolay(ydat,
npoints=npoints,
degree=degree,
order=order)
else:
raise NameError(
"SGModule is not available -- this operation cannot be performed!")
def estimateXANESEdge(spectrum, energy=None, full=False):
if energy is None:
x = numpy.arange(len(spectrum)).astype(numpy.float)
else:
x = numpy.array(energy, dtype=numpy.float, copy=True)
y = numpy.array(spectrum, dtype=numpy.float, copy=True)
# make sure data are sorted
idx = energy.argsort(kind='mergesort')
x = numpy.take(energy, idx)
y = numpy.take(spectrum, idx)
# make sure data are strictly increasing
delta = x[1:] - x[:-1]
dmin = delta.min()
dmax = delta.max()
if delta.min() <= 1.0e-10:
# force data are strictly increasing
# although we do not consider last point
idx = numpy.nonzero(delta>0)[0]
x = numpy.take(x, idx)
y = numpy.take(y, idx)
delta = None
sortedX = x
sortedY = y
# use a regularly spaced spectrum
if dmax != dmin:
# choose the number of points or deduce it from
# the input data length?
nchannels = 2 * len(spectrum)
xi = numpy.linspace(x[1], x[-2], nchannels).reshape(-1, 1)
x.shape = -1
y.shape = -1
y = SpecfitFuns.interpol([x], y, xi, y.min())
x = xi
x.shape = -1
y.shape = -1
# take the first derivative
npoints = 7
xPrime = x[npoints:-npoints]
yPrime = SGModule.getSavitzkyGolay(y, npoints=npoints, degree=2, order=1)
# get the index at maximum value
iMax = numpy.argmax(yPrime)
# get the center of mass
w = 2 * npoints
selection = yPrime[iMax-w:iMax+w+1]
edge = (selection * xPrime[iMax-w:iMax+w+1]).sum(dtype=numpy.float)/\
selection.sum(dtype=numpy.float)
if full:
# return intermediate information
return edge, sortedX, sortedY, xPrime, yPrime
else:
# return the corresponding x value
return edge
def estimateXANESEdge(spectrum, energy=None, full=False):
if energy is None:
x = numpy.arange(len(spectrum)).astype(numpy.float)
else:
x = numpy.array(energy, dtype=numpy.float, copy=True)
y = numpy.array(spectrum, dtype=numpy.float, copy=True)
# make sure data are sorted
idx = energy.argsort(kind='mergesort')
x = numpy.take(energy, idx)
y = numpy.take(spectrum, idx)
# make sure data are strictly increasing
delta = x[1:] - x[:-1]
dmin = delta.min()
dmax = delta.max()
if delta.min() <= 1.0e-10:
# force data are strictly increasing
# although we do not consider last point
idx = numpy.nonzero(delta > 0)[0]
x = numpy.take(x, idx)
y = numpy.take(y, idx)
delta = None
sortedX = x
sortedY = y
# use a regularly spaced spectrum
if dmax != dmin:
# choose the number of points or deduce it from
# the input data length?
nchannels = 2 * len(spectrum)
xi = numpy.linspace(x[1], x[-2], nchannels).reshape(-1, 1)
x.shape = -1
y.shape = -1
y = SpecfitFuns.interpol([x], y, xi, y.min())
x = xi
x.shape = -1
y.shape = -1
# take the first derivative
npoints = 7
xPrime = x[npoints:-npoints]
yPrime = SGModule.getSavitzkyGolay(y, npoints=npoints, degree=2, order=1)
# get the index at maximum value
iMax = numpy.argmax(yPrime)
# get the center of mass
w = 2 * npoints
selection = yPrime[iMax - w:iMax + w + 1]
edge = (selection * xPrime[iMax-w:iMax+w+1]).sum(dtype=numpy.float)/\
selection.sum(dtype=numpy.float)
if full:
# return intermediate information
return edge, sortedX, sortedY, xPrime, yPrime
else:
# return the corresponding x value
return edge
def getE0SavitzkyGolay(energy, mu, points=5, full=False):
# It does not check anything, data have to be prepared before!!!
# take the first derivative
yPrime = SGModule.getSavitzkyGolay(mu, npoints=points, degree=2, order=1)
xPrime = energy[:]
# get the index at maximum value
iMax = numpy.argmax(yPrime)
# get the center of mass
w = points
selection = yPrime[iMax - w : iMax + w + 1]
edge = (selection * xPrime[iMax - w : iMax + w + 1]).sum(dtype=numpy.float) / selection.sum(dtype=numpy.float)
if full:
# return intermediate information
return {"edge": edge, "iMax": iMax, "xPrime": xPrime, "yPrime": yPrime}
else:
# return the corresponding x value
return edge
def updateGraph(self, ddict):
points = ddict['points']
degree = ddict['degree']
order = ddict['order']
self.background = SGModule.getSavitzkyGolay(self.spectrum,
points,
degree=degree,
order=order)
if order > 0:
maptoy2 = True
else:
maptoy2 = False
self.graph.newCurve(self.xValues,
self.background, "Filtered Spectrum",
replace=False,
maptoy2=maptoy2)
#Force information update
legend = self.graph.getActiveCurve(just_legend=True)
if legend.startswith('Filtered'):
self.graph.setActiveCurve(legend)
def updateGraph(self, ddict):
points = ddict['points']
degree = ddict['degree']
order = ddict['order']
self.background = SGModule.getSavitzkyGolay(self.spectrum,
points,
degree=degree,
order=order)
if order > 0:
yaxis = "right"
else:
yaxis = "left"
self.graph.addCurve(self.xValues,
self.background,
"Filtered Spectrum",
replace=False,
yaxis=yaxis)
#Force information update
legend = self.graph.getActiveCurve(just_legend=True)
if legend.startswith('Filtered'):
self.graph.setActiveCurve(legend)
def _pymca_SG(ydat, npoints=3, degree=1, order=0):
"""call to symmetric Savitzky-Golay filter in PyMca
Parameters
----------
ydat : 1D array contaning the data to smooth
npoints : integer [3], means that 2*npoints+1 values contribute
to the smoother.
degree : degree of fitting polynomial
order : is degree of implicit differentiation
0 means that filter results in smoothing of function
1 means that filter results in smoothing the first
derivative of function.
and so on ...
Returns
-------
ys : smoothed array
"""
if HAS_SGMODULE:
return SGModule.getSavitzkyGolay(ydat, npoints=npoints, degree=degree, order=order)
else:
raise NameError("SGModule is not available -- this operation cannot be performed!")
def getE0SavitzkyGolay(energy, mu, points=5, full=False):
# It does not check anything, data have to be prepared before!!!
# take the first derivative
yPrime = SGModule.getSavitzkyGolay(mu, npoints=points, degree=2, order=1)
xPrime = energy[:]
# get the index at maximum value
iMax = numpy.argmax(yPrime)
# get the center of mass
w = points
selection = yPrime[iMax-w:iMax+w+1]
edge = (selection * xPrime[iMax-w:iMax+w+1]).sum(dtype=numpy.float64)/\
selection.sum(dtype=numpy.float64)
if full:
# return intermediate information
return {"edge":edge,
"iMax": iMax,
"xPrime": xPrime,
"yPrime": yPrime}
else:
# return the corresponding x value
return edge
