def shiftBilinear(img, shift):
"""
Shift an array like scipy.ndimage.interpolation.shift(input, shift, mode="wrap", order="infinity")
:param input: 2d numpy array
:param shift: 2-tuple of float
:return: shifted image
"""
shape = img.shape
x = numpy.zeros((shape[0] * shape[1], 2), numpy.float)
x[:,0] = shift[0] + numpy.outer(numpy.arange(shape[0]), numpy.ones(shape[1])).reshape(-1)
x[:,1] = shift[1] + numpy.outer(numpy.ones(shape[0]), numpy.arange(shape[1])).reshape(-1)
shifted = SpecfitFuns.interpol([numpy.arange(shape[0]),
numpy.arange(shape[1])], img, x)
shifted.shape = shape[0], shape[1]
return shifted
def shiftBilinear(img, shift):
"""
Shift an array like scipy.ndimage.interpolation.shift(input, shift, mode="wrap", order="infinity")
:param input: 2d numpy array
:param shift: 2-tuple of float
:return: shifted image
"""
shape = img.shape
x = numpy.zeros((shape[0] * shape[1], 2), numpy.float)
x[:, 0] = shift[0] + numpy.outer(numpy.arange(shape[0]),
numpy.ones(shape[1])).reshape(-1)
x[:, 1] = shift[1] + numpy.outer(numpy.ones(shape[0]),
numpy.arange(shape[1])).reshape(-1)
shifted = SpecfitFuns.interpol(
[numpy.arange(shape[0]),
numpy.arange(shape[1])], img, x)
shifted.shape = shape[0], shape[1]
return shifted
def slit(self,pars,x):
"""
Function to calulate slit width and knife edge cut
"""
return 0.5*SpecfitFuns.slit(pars,x)
def stepUp(self,pars,x):
"""
Error function like.
"""
return 0.5*SpecfitFuns.upstep(pars,x)
def stepDown(self,pars,x):
"""
Complementary error function like.
"""
return 0.5*SpecfitFuns.downstep(pars,x)
def hypermet(self,pars,x):
"""
Default hypermet function
"""
return SpecfitFuns.ahypermet(pars, x, 15, 0)
def _getStripBackground(self, x=None, y=None):
#this makes the assumption x are equally spaced
#and I should build a spline if that is not the case
#but I do not want to put a dependency on SciPy
if y is not None:
ywork = y
else:
ywork = self._y
if x is not None:
xwork = x
else:
xwork = self._x
n=len(xwork)
#loop for anchors
anchorslist = []
if self._fitConfiguration['fit']['stripanchorsflag']:
if self._fitConfiguration['fit']['stripanchorslist'] is not None:
oldShape = xwork.shape
ravelled = xwork
ravelled.shape = -1
for channel in self._fitConfiguration['fit']['stripanchorslist']:
if channel <= ravelled[0]:continue
index = numpy.nonzero(ravelled >= channel)
if len(index):
index = min(index)
if index > 0:
anchorslist.append(index)
ravelled.shape = oldShape
#work with smoothed data
ysmooth = self._getSmooth(xwork, ywork)
#SNIP algorithm
if self._fitConfiguration['fit']['stripalgorithm'] in ["SNIP", 1]:
if DEBUG:
print("CALCULATING SNIP")
if len(anchorslist) == 0:
anchorslist = [0, len(ysmooth)-1]
anchorslist.sort()
result = 0.0 * ysmooth
lastAnchor = 0
width = self._fitConfiguration['fit']['snipwidth']
for anchor in anchorslist:
if (anchor > lastAnchor) and (anchor < len(ysmooth)):
result[lastAnchor:anchor] =\
SpecfitFuns.snip1d(ysmooth[lastAnchor:anchor], width, 0)
lastAnchor = anchor
if lastAnchor < len(ysmooth):
result[lastAnchor:] =\
SpecfitFuns.snip1d(ysmooth[lastAnchor:], width, 0)
return result
#strip background
niter = self._fitConfiguration['fit']['stripiterations']
if niter > 0:
if DEBUG:
print("CALCULATING STRIP")
print("iterations = ", niter)
print("constant = ",
self._fitConfiguration['fit']['stripconstant'])
print("width = ",
self._fitConfiguration['fit']['stripwidth'])
print("anchors = ", anchorslist)
result=SpecfitFuns.subac(ysmooth,
self._fitConfiguration['fit']['stripconstant'],
niter,
self._fitConfiguration['fit']['stripwidth'],
anchorslist)
if niter > 1000:
#make sure to get something smooth
result = SpecfitFuns.subac(result,
self._fitConfiguration['fit']['stripconstant'],
500,1,
anchorslist)
else:
#make sure to get something smooth but with less than
#500 iterations
result = SpecfitFuns.subac(result,
self._fitConfiguration['fit']['stripconstant'],
int(self._fitConfiguration['fit']['stripwidth']*2),
1,
anchorslist)
else:
if DEBUG:
print("NO STRIP, NO SNIP")
result = numpy.zeros(ysmooth.shape, numpy.float) + min(ysmooth)
return result
self.okButton.clicked.connect(self.accept)
def getParameters(self):
parametersDict = self.parametersWidget.getParameters()
return parametersDict
def setParameters(self, ddict):
return self.parametersWidget.setParameters(ddict)
if __name__ == "__main__":
app = qt.QApplication([])
if len(sys.argv) > 1:
from PyMca5.PyMcaIO import specfilewrapper as specfile
sf = specfile.Specfile(sys.argv[1])
scan = sf[0]
data = scan.data()
energy = data[0, :]
spectrum = data[1, :]
w = XASNormalizationDialog(None, spectrum, energy=energy)
else:
from PyMca5 import SpecfitFuns
noise = numpy.random.randn(1500.)
x = 8000. + numpy.arange(1500.)
y = SpecfitFuns.upstep([100, 8500., 50], x)
w = XASNormalizationDialog(None, y + numpy.sqrt(y)* noise, energy=x)
ret=w.exec_()
if ret:
print(w.getParameters())
def getParameters(self):
parametersDict = self.parametersWidget.getParameters()
return parametersDict
def setParameters(self, ddict):
return self.parametersWidget.setParameters(ddict)
def setSpectrum(self, energy, mu):
self.parametersWidget.setData(mu, energy=energy)
if __name__ == "__main__":
app = qt.QApplication([])
if len(sys.argv) > 1:
from PyMca5.PyMcaIO import specfilewrapper as specfile
sf = specfile.Specfile(sys.argv[1])
scan = sf[0]
data = scan.data()
energy = data[0, :]
spectrum = data[1, :]
w = XASNormalizationDialog(None, spectrum, energy=energy)
else:
from PyMca5 import SpecfitFuns
noise = numpy.random.randn(1500)
x = 8000. + numpy.arange(1500.)
y = SpecfitFuns.upstep([100, 8500., 50], x)
w = XASNormalizationDialog(None, y + numpy.sqrt(y) * noise, energy=x)
ret = w.exec_()
if ret:
print(w.getParameters())
def fftAlignment(self):
curves = self.getMonotonicCurves()
nCurves = len(curves)
if nCurves < 2:
raise ValueError("At least 2 curves needed")
return
# get legend of active curve
try:
activeCurveLegend = self.getActiveCurve()[2]
if activeCurveLegend is None:
activeCurveLegend = curves[0][2]
for curve in curves:
if curve[2] == activeCurveLegend:
activeCurve = curve
break
except:
activeCurve = curves[0]
# apply between graph limits
x0, y0 = activeCurve[0:2]
xmin, xmax =self.getGraphXLimits()
for curve in curves:
xmax = float(min(xmax, curve[0][-1]))
xmin = float(max(xmin, curve[0][0]))
idx = numpy.nonzero((x0 >= xmin) & (x0 <= xmax))[0]
x0 = numpy.take(x0, idx)
y0 = numpy.take(y0, idx)
#make sure values are regularly spaced
xi = numpy.linspace(x0[0], x0[-1], len(idx)).reshape(-1, 1)
yi = SpecfitFuns.interpol([x0], y0, xi, y0.min())
x0 = xi * 1
y0 = yi * 1
y0.shape = -1
fft0 = numpy.fft.fft(y0)
y0.shape = -1, 1
x0.shape = -1, 1
nChannels = x0.shape[0]
# built a couple of temporary array of spectra for handy access
shiftList = []
curveList = []
i = 0
for idx in range(nCurves):
x, y, legend, info = curves[idx][0:4]
if x.size != x0.size:
needInterpolation = True
elif numpy.allclose(x, x0):
# no need for interpolation
needInterpolation = False
else:
needInterpolation = True
if needInterpolation:
# we have to interpolate
x.shape = -1
y.shape = -1
xi = x0[:] * 1
y = SpecfitFuns.interpol([x], y, xi, y0.min())
x = xi
y.shape = -1
i += 1
# now calculate the shift
ffty = numpy.fft.fft(y)
if numpy.allclose(fft0, ffty):
shiftList.append(0.0)
else:
shift = numpy.fft.ifft(fft0 * ffty.conjugate()).real
shift2 = numpy.zeros(shift.shape, dtype=shift.dtype)
m = shift2.size//2
shift2[m:] = shift[:-m]
shift2[:m] = shift[-m:]
threshold = 0.80*shift2.max()
#threshold = shift2.mean()
idx = numpy.nonzero(shift2 > threshold)[0]
#print("max indices = %d" % (m - idx))
shift = (shift2[idx] * idx/shift2[idx].sum()).sum()
#print("shift = ", shift - m, "in x units = ", (shift - m) * (x[1]-x[0]))
# shift the curve
shift = (shift - m) * (x[1]-x[0])
x.shape = -1
y = numpy.fft.ifft(numpy.exp(-2.0*numpy.pi*numpy.sqrt(numpy.complex(-1))*\
numpy.fft.fftfreq(len(x), d=x[1]-x[0])*shift)*ffty)
y = y.real
y.shape = -1
curveList.append([x, y, legend + "SHIFT", False, False])
curveList[-1][-2] = True
curveList[-1][-1] = False
x, y, legend, replot, replace = curveList[0]
self.addCurve(x, y, legend=legend, replot=True, replace=True)
for i in range(1, len(curveList)):
x, y, legend, replot, replace = curveList[i]
self.addCurve(x,
y,
legend=legend,
info=None,
replot=replot,
replace=False)
return
