def diff_tth_X(self, dx=0.1):
"""
Jerome peux-tu décrire de quoi il retourne ???
:param dx: ???
:type: float ???
:return: ???
:rtype: ???
"""
f = self.ai.getFit2D()
fp = f.copy()
fm = f.copy()
fm["centerX"] -= dx / 2.0
fp["centerX"] += dx / 2.0
ap = AzimuthalIntegrator()
am = AzimuthalIntegrator()
ap.setFit2D(**fp)
am.setFit2D(**fm)
dtthX = (ap.twoThetaArray(self.shape) - am.twoThetaArray(self.shape))\
/ dx
tth, I = self.ai.xrpd(self.img, max(self.shape))
dI = SGModule.getSavitzkyGolay(I, npoints=5, degree=2, order=1)\
/ (tth[1] - tth[0])
dImg = self.reconstruct(tth, dI)
return (dtthX * dImg).sum()
def diff_Fit2D(self, axis="all", dx=0.1):
"""
???
@param axis: ???
@type axis: ???
@param dx: ???
@type dx: ???
@return: ???
@rtype: ???
"""
tth, I = self.ai.xrpd(self.img, max(self.shape))
dI = SGModule.getSavitzkyGolay(I, npoints=5, degree=2, order=1) / (tth[1] - tth[0])
dImg = self.reconstruct(tth, dI)
f = self.ai.getFit2D()
tth2d_ref = self.ai.twoThetaArray(self.shape) # useless variable ???
keys = ["centerX", "centerY", "tilt", "tiltPlanRotation"]
if axis != "all":
keys = [i for i in keys if i == axis]
grad = {}
for key in keys:
fp = f.copy()
fp[key] += dx
ap = AzimuthalIntegrator()
ap.setFit2D(**fp)
dtth = (ap.twoThetaArray(self.shape) - self.ai.twoThetaArray(self.shape)) / dx
grad[key] = (dtth * dImg).sum()
if axis == "all":
return grad
else:
return grad[axis]
def diff_Fit2D(self, axis="all", dx=0.1):
"""
???
:param axis: ???
:type axis: ???
:param dx: ???
:type dx: ???
:return: ???
:rtype: ???
"""
tth, I = self.ai.xrpd(self.img, max(self.shape))
dI = SGModule.getSavitzkyGolay(I, npoints=5, degree=2, order=1)\
/ (tth[1] - tth[0])
dImg = self.reconstruct(tth, dI)
f = self.ai.getFit2D()
_tth2d_ref = self.ai.twoThetaArray(self.shape) # useless variable ???
keys = ["centerX", "centerY", "tilt", "tiltPlanRotation"]
if axis != "all":
keys = [i for i in keys if i == axis]
grad = {}
for key in keys:
fp = f.copy()
fp[key] += dx
ap = AzimuthalIntegrator()
ap.setFit2D(**fp)
dtth = (ap.twoThetaArray(self.shape) -
self.ai.twoThetaArray(self.shape)) / dx
grad[key] = (dtth * dImg).sum()
if axis == "all":
return grad
else:
return grad[axis]
def diff_tth_X(self, dx=0.1):
"""
Jerome peux-tu décrire de quoi il retourne ???
:param dx: ???
:type: float ???
:return: ???
:rtype: ???
"""
f = self.ai.getFit2D()
fp = f.copy()
fm = f.copy()
fm["centerX"] -= dx / 2.0
fp["centerX"] += dx / 2.0
ap = AzimuthalIntegrator()
am = AzimuthalIntegrator()
ap.setFit2D(**fp)
am.setFit2D(**fm)
dtthX = (ap.twoThetaArray(self.shape) - am.twoThetaArray(self.shape))\
/ dx
tth, I = self.ai.xrpd(self.img, max(self.shape))
dI = SGModule.getSavitzkyGolay(I, npoints=5, degree=2, order=1)\
/ (tth[1] - tth[0])
dImg = self.reconstruct(tth, dI)
return (dtthX * dImg).sum()
def diff_tth_tilt(self, dx=0.1):
"""
idem ici ???
@param dx: ???
@type dx: float ???
@return: ???
@rtype: ???
"""
f = self.ai.getFit2D()
fp = f.copy()
fm = f.copy()
fm["tilt"] -= dx / 2.0
fp["tilt"] += dx / 2.0
ap = AzimuthalIntegrator()
am = AzimuthalIntegrator()
ap.setFit2D(**fp)
am.setFit2D(**fm)
dtthX = (ap.twoThetaArray(self.shape) - am.twoThetaArray(self.shape))\
/ dx
tth, I = self.ai.xrpd(self.img, max(self.shape))
dI = SGModule.getSavitzkyGolay(I, npoints=5, degree=2, order=1)\
/ (tth[1] - tth[0])
dImg = self.reconstruct(tth, dI)
return (dtthX * dImg).sum()
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 = 5 * 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)
if full:
# return intermediate information
return x[iMax], sortedX, sortedY, xPrime, yPrime
else:
# return the corresponding x value
return x[iMax]
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)
