def __init__(self, axis=None, angle=None, R=None):
if isinstance(R, dnp.ndarray):
if R.shape != (3, 3):
raise Exception('R must be a 3 by 3 matrix')
self.R = R
angle = dnp.arccos(0.5*(sum(dnp.diag(R))-1))
self.angle = float(dnp.rad2deg(angle))
x = (R[2][1]-R[1][2])/(2*dnp.sin(angle))
y = (R[0][2]-R[2][0])/(2*dnp.sin(angle))
z = (R[1][0]-R[0][1])/(2*dnp.sin(angle))
axis = Vector(x,y,z)
axis = axis.unit()
self.axis = axis
return
if isinstance(angle, dnp.ndarray):
angle = angle[0]
elif isinstance(angle, int):
angle = float(angle)
if not isinstance(angle, float):
raise Exception('angle must be a float not {}'.format(type(angle)))
axis = axis.unit()
self.axis = axis
self.angle = angle
angle = float(dnp.radians(angle))
M = dnp.array([[0.0, -axis.z, axis.y],
[axis.z, 0.0, -axis.x],
[-axis.y, axis.x, 0.0]])
N = axis.tensor_product(axis)
I = dnp.identity(3)
self.R = I*dnp.cos(angle) + M*dnp.sin(angle) + (1-dnp.cos(angle))*N
angle = 56
def get_chi_steps(two_theta, starting_chi_value=100.0, final_chi_value=0.0):
theta = dnp.radians(two_theta / 2.0) # converts to radians and halfs to theta
radius = LENGTH * float(dnp.sin(theta))
delta_chi = float(dnp.rad2deg((WIDTH / radius)))
number_of_steps = dnp.abs(final_chi_value-starting_chi_value)/(delta_chi*0.5)
number_of_steps = int(number_of_steps)+1
return dnp.linspace(starting_chi_value, final_chi_value, number_of_steps)
def rawGetPosition(self):
wlen = self.getWaveLenght()
# twoTheta = self.twoThOff + 2.0*theta*self.twoThSign
theta = self.twoThSign * (float(self.tth.getPosition()) -
self.twoThOff) / 2.0
self.currentposition = float(4.0 * dnp.pi / wlen *
dnp.sin(theta * dnp.pi / 180))
return self.currentposition
def dofourier(xarray, yarray, printres=False):
nvals = len(yarray)
avht = yarray.mean()
angles = dnp.linspace(0, 2 * dnp.pi, len(yarray))
# print angles
ycarray = yarray - avht
cosines = dnp.cos(angles)
sines = dnp.sin(angles)
cosint = cosines * ycarray
sinint = sines * ycarray
if printres:
print "Yarray:", yarray
print "Ycarray:", ycarray
print "Sinint:", sinint
print "Cosint:", cosint
cosum = cosint[:-1].sum()
sinsum = sinint[:-1].sum()
cosfactor = cosum / float(nvals - 1)
sinfactor = sinsum / float(nvals - 1)
if printres:
print "sinfactor, cosfactor: %.4f %.4f" % (sinfactor, cosfactor)
ratio = sinfactor / cosfactor
delta = math.atan(ratio)
deltadeg = math.degrees(delta)
if printres:
print "Delta degrees %06.4f" % deltadeg
halfmag = math.sqrt(sinfactor ** 2 + cosfactor ** 2)
mag = 2.0 * halfmag
if printres:
print "Magnitude: %06.4f" % mag
print "vertical centre %7.4f" % avht
calcresult = avht + mag * dnp.cos(angles - delta)
if printres:
print "Calcresult:", calcresult
for idx in range(0, len(yarray)):
print yarray[idx], calcresult[idx]
return (avht, mag, delta, calcresult)
def dofourier(xarray,yarray,printres=False):
nvals=len(yarray)
avht=yarray.mean()
angles=dnp.linspace(0,2*dnp.pi,len(yarray))
#print angles
ycarray=yarray-avht
cosines=dnp.cos(angles)
sines=dnp.sin(angles)
cosint=cosines*ycarray
sinint=sines*ycarray
if printres:
print "Yarray:",yarray
print "Ycarray:",ycarray
print "Sinint:",sinint
print "Cosint:",cosint
cosum=cosint[:-1].sum()
sinsum=sinint[:-1].sum()
cosfactor=cosum/float(nvals-1)
sinfactor=sinsum/float(nvals-1)
if printres:
print "sinfactor, cosfactor: %.4f %.4f"%(sinfactor,cosfactor)
ratio=sinfactor/cosfactor
delta=math.atan(ratio)
deltadeg=math.degrees(delta)
if printres:
print"Delta degrees %06.4f"%deltadeg
halfmag=math.sqrt(sinfactor**2+cosfactor**2)
mag=2.0*halfmag
if printres:
print"Magnitude: %06.4f"%mag
print "vertical centre %7.4f"%avht
calcresult=avht+mag*dnp.cos(angles-delta)
if printres:
print"Calcresult:",calcresult
for idx in range(0,len(yarray)):
print yarray[idx],calcresult[idx]
return(avht,mag,delta,calcresult)
def project(self, energy, UB, pixels, gamma, delta, omega, alpha, nu):
# put the detector at the right position
dx, dy, dz = pixels
# convert angles to radians
gamma, delta, alpha, omega, nu = numpy.radians(
(gamma, delta, alpha, omega, nu))
RGam = numpy.matrix([[1, 0, 0], [0, cos(gamma), -sin(gamma)],
[0, sin(gamma), cos(gamma)]])
RDel = (numpy.matrix([[cos(delta), -sin(delta), 0],
[sin(delta), cos(delta), 0], [0, 0, 1]])).getI()
RNu = numpy.matrix([[cos(nu), 0, sin(nu)], [0, 1, 0],
[-sin(nu), 0, cos(nu)]])
# calculate Cartesian coordinates for each pixel using clever matrix stuff
M = numpy.mat(
numpy.concatenate(
(dx.flatten(0), dy.flatten(0),
dz.flatten(0))).reshape(3, dx.shape[0] * dx.shape[1]))
XYZp = RGam * RDel * RNu * M
xp = dnp.array(XYZp[0]).reshape(dx.shape)
yp = dnp.array(XYZp[1]).reshape(dy.shape)
zp = dnp.array(XYZp[2]).reshape(dz.shape)
# don't bother with the part about slits...
# Calculate effective gamma and delta for each pixel
d_ds = dnp.sqrt(xp**2 + yp**2 + zp**2)
Gam = dnp.arctan2(zp, yp)
Del = -1 * dnp.arcsin(-xp / d_ds)
# wavenumber
k = 2 * math.pi / 12.398 * energy
# Define the needed matrices. The notation follows the article by Bunk &
# Nielsen. J.Appl.Cryst. (2004) 37, 216-222.
M1 = k * numpy.matrix(
cos(omega) * sin(Del) - sin(omega) *
(cos(alpha) *
(cos(Gam) * cos(Del) - 1) + sin(alpha) * sin(Gam) * cos(Del)))
M2 = k * numpy.matrix(
sin(omega) * sin(Del) + cos(omega) *
(cos(alpha) *
(cos(Gam) * cos(Del) - 1) + sin(alpha) * sin(Gam) * cos(Del)))
M3 = k * numpy.matrix(-sin(alpha) * (cos(Gam) * cos(Del) - 1) +
cos(alpha) * sin(Gam) * cos(Del))
# invert UB matrix
UBi = numpy.matrix(UB).getI()
# calculate HKL
H = UBi[0, 0] * M1 + UBi[0, 1] * M2 + UBi[0, 2] * M3
K = UBi[1, 0] * M1 + UBi[1, 1] * M2 + UBi[1, 2] * M3
L = UBi[2, 0] * M1 + UBi[2, 1] * M2 + UBi[2, 2] * M3
return (H, K, L)
include('workspace://Utilities/dawnplotting.py')
import scisoftpy as dnp
t = dnp.arange(0.0, 2.0, 0.01)
s = dnp.sin(2 * dnp.pi * t)
dnp.plot.line(t, s)
ps = dnp.plot.getPlottingSystem()
ps.setTitle("EclipseCon France Demo")
def refl(runfiles, pathtofiles, outputpath, scalar, beamheight, footprint, angularfudgefactor, wl, back=()):
# scalar - scale factor to divide the data by
# beamheight FWHM in microns
# footprint in mm
# angular offset correction in degrees
# wavelength in wl
# back is an optional variable to subtract a background, set back=1 to do a background subtraction
qq = []
RR = []
dR = []
ii = -1
for filename in runfiles:
data = dnp.io.load(pathtofiles + "/" + str(filename) + ".dat")
ii += 1
theta = data.alpha
# work out the q vector
qqtemp = 4 * dnp.pi * dnp.sin((theta + angularfudgefactor) * dnp.pi /180) / wl
#qqtemp = data.qdcd
# this section is to allow users to set limits on the q range used from each file
if not 'qmin' + str(ii) in refl.__dict__:
qmin = qqtemp.min()
else:
print "USER SET",
qmin = refl.__getattribute__('qmin' + str(ii))
print 'refl.qmin' + str(ii) + " = " + str(qmin) + " ;",
if not 'qmax' + str(ii) in refl.__dict__:
qmax = qqtemp.max()
else:
print "USER SET",
qmax = refl.__getattribute__('qmax' + str(ii))
print 'refl.qmax' + str(ii) + " = " + str(qmax) + " ;",
roi1_sum = data.roi1_sum
roi1_sum = roi1_sum[dnp.where((qqtemp >= qmin) & (qqtemp <= qmax))]
roi1dr = dnp.sqrt(roi1_sum)
theta = theta[dnp.where((qqtemp >= qmin) & (qqtemp <= qmax))]
qqtemp = qqtemp[dnp.where((qqtemp >= qmin) & (qqtemp <= qmax))]
bg_sum = dnp.zeros(len(roi1_sum))
bg_dr = dnp.zeros(len(roi1dr))
# if background ROI number given as int, convert to a single-item tuple
if type(back) == int:
back = (back,)
# subtract any background ROIs from the data
if len(back) > 0 and back[0] > 0:
if ii==0:
print "Using background from " + str(len(back)) + " ROIs: " + str(back)
for bg in back:
if ('roi' + str(bg) + '_sum' in data.keys()):
bg_cur = data[data.keys().index('roi' +str(bg) + '_sum')]
dr_cur = dnp.sqrt(bg_cur)
(bg_sum, bg_dr) = ep.EPadd(bg_sum, bg_dr, bg_cur, dr_cur)
(bg_sum, bg_dr) = ep.EPmulk(bg_sum, bg_dr, 1.0/len(back))
else:
if ii==0:
print "Not subtracting a background"
(RRtemp, drtemp) = ep.EPsub(roi1_sum, roi1dr, bg_sum, bg_dr)
# do a footprint correction.
# assumes that the beam is gaussian in profile, with a FWHM of "beamheight".
# footprint of sample is measured in mm.
areamultiplier = 2*(norm.cdf(footprint * dnp.sin((theta + angularfudgefactor) / 180 * dnp.pi) / 2, 0, 1e-3 * beamheight/ (2*dnp.sqrt(2*dnp.log(2)))) - 0.5)
RRtemp /= areamultiplier
drtemp /= areamultiplier
# for the 2nd, 3rd, 4th q ranges have to splice the data on the end of the preexisting data
if(ii > 0):
# splice
(scalingfactor, sferror) = nsplice.getScalingInOverlap(qq, RR, dR, qqtemp, RRtemp, drtemp)
RRtemp *= scalingfactor
drtemp *= scalingfactor
print "Error in scaling factor: %2.3f %%" % (sferror/scalingfactor*100)
# now concatenate the data.
qq = dnp.concatenate((qq, qqtemp))
RR = dnp.concatenate((RR, RRtemp))
dR = dnp.concatenate((dR, drtemp))
# end of per-file loop
RR /= scalar
dR /= scalar
RR = RR[np.argsort(qq)]
dR = dR[np.argsort(qq)]
qq = np.sort(qq)
# write out the data.
np.savetxt(outputpath+"/"+str(runfiles[0])+"_refl.dat",dnp.concatenate((qq,RR,dR)).reshape(3,qq.shape[0]).transpose())
def rerefl(runfiles):
global pathtofiles, savedbean, firstfiledata
qq = []
RR = []
dR = []
ii = -1
for filename in runfiles:
roi1sum = []
roi1dr = []
bg_sum = []
bg_dr = []
data = dnp.io.load(pathtofiles + "/" + str(filename) + ".dat")
ii += 1
if ii == 0:
firstfiledata = data
# define theta
theta = data.alpha
# define q
qqtemp = 4 * dnp.pi * dnp.sin((theta + angularfudgefactor) * dnp.pi /180) / wl
#qqtemp = data.qdcd
qmin = qqtemp.min()
qmax = qqtemp.max()
# plot first image of first file
if ii == 0:
global image
image = dnp.io.load(replace_path(data['file'][imgdisp]), warn=False)
dnp.plot.image(image[0], name='Plot 1', resetaxes=False)
# find ROIs from saved bean
try:
rois = dnp.plot.getrois(savedbean)
norois = len(rois)
# if we don't have any ROIs yet, ask the user to draw some
except(KeyError, TypeError, NameError):
if ii==0:
print "\nPlease define some regions of interest then type getrois()"
print "You must type getrois() after adding/changing any regions due to a bug in DAWN."
norois = 0
# this section to be restored when ROIs are working again
## find ROIs from plot window
#bean = dnp.plot.getbean('Plot 1')
#try:
# rois = dnp.plot.getrois(bean)
# norois = len(rois)
## if we don't have any ROIs yet, ask the user to draw some
#except(KeyError, TypeError):
# if ii==0:
# print "Please define some regions of interest"
# norois = 0
if norois > 0:
if ii == 0:
print str(norois) + " ROIs defined, " + str(norois-1) + " will be used for the background"
for imgfile in data['file']:
imgdata = dnp.io.load(replace_path(imgfile), warn=False)
dnp.plot.image(imgdata[0], name="Plot 1", resetaxes=False)
image = imgdata[0].transpose() # Pilatus images load with axes transposed for some reason
bg_pt = 0
bgdr_pt = 0
for j in range(0,norois):
roi = image[int(rois[j].spt[0]):int(rois[j].spt[0]+rois[j].len[0]), int(rois[j].spt[1]):int(rois[j].spt[1]+rois[j].len[1])]
roisum_pt = dnp.float(roi.sum())
if j == 0:
roi1sum.append(roisum_pt)
roi1dr.append(dnp.sqrt(roisum_pt))
else:
(bg_pt, bgdr_pt) = ep.EPadd(bg_pt, bgdr_pt, roisum_pt, dnp.sqrt(roisum_pt))
bg_sum.append(bg_pt)
bg_dr.append(bgdr_pt)
# convert lists to arrays
(roi1sum, roi1dr, bg_sum, bg_dr) = (dnp.array(roi1sum), dnp.array(roi1dr), dnp.array(bg_sum), dnp.array(bg_dr))
# normalise background
if norois > 1:
bgsize = 0
for k in range(1, norois):
bgsize += rois[k].len[0]*rois[k].len[1]
(bg_sum, bg_dr) = ep.EPmulk(bg_sum, bg_dr, rois[0].len[0]*rois[0].len[1]/bgsize)
# subtract background
(RRtemp, drtemp) = ep.EPsub(roi1sum, roi1dr, bg_sum, bg_dr)
# do a footprint correction.
# assumes that the beam is gaussian in profile, with a FWHM of "beamheight".
# footprint of sample is measured in mm.
areamultiplier = 2*(norm.cdf(dnp.float(footprint) * dnp.sin((theta + dnp.float(angularfudgefactor)) / 180 * dnp.pi) / 2, 0, 1e-3 * dnp.float(beamheight)/ (2*dnp.sqrt(2*dnp.log(2)))) - 0.5)
RRtemp /= areamultiplier
drtemp /= areamultiplier
# for the 2nd, 3rd, 4th q ranges have to splice the data on the end of the preexisting data
if(ii > 0):
(scalingfactor, sferror) = nsplice.getScalingInOverlap(qq, RR, dR, qqtemp, RRtemp, drtemp)
RRtemp *= scalingfactor
drtemp *= scalingfactor
# now concatenate the data.
qq = dnp.concatenate((qq, qqtemp))
RR = dnp.concatenate((RR, RRtemp))
dR = dnp.concatenate((dR, drtemp))
# end of per-file loop
if norois > 0:
RR /= dnp.float(scalar)
dR /= dnp.float(scalar)
RR = RR[np.argsort(qq)]
dR = dR[np.argsort(qq)]
qq = np.sort(qq)
# write out the data.
np.savetxt(outputpath+"/"+str(runfiles[0])+"_rerefl_bkg1.dat",dnp.concatenate((qq,RR,dR)).reshape(3,qq.shape[0]).transpose(), fmt="%.10f %.10e %.10e")
print "Output saved to " + outputpath+"/"+str(runfiles[0])+"_rerefl_bkg1.dat"
# plot the resulting
dnp.plot.line(qq,dnp.log10(RR),name='Plot 2')
def project(self, energy, UB, pixels, gamma, delta, omega, alpha, nu):
# put the detector at the right position
dx,dy,dz = pixels
# convert angles to radians
gamma, delta, alpha, omega, nu = numpy.radians((gamma, delta, alpha, omega, nu))
RGam = numpy.matrix([[1,0,0],[0,cos(gamma),-sin(gamma)],[0,sin(gamma),cos(gamma)]])
RDel = (numpy.matrix([[cos(delta),-sin(delta),0],[sin(delta),cos(delta),0],[0,0,1]])).getI()
RNu = numpy.matrix([[cos(nu),0,sin(nu)],[0,1,0],[-sin(nu),0,cos(nu)]])
# calculate Cartesian coordinates for each pixel using clever matrix stuff
M = numpy.mat(numpy.concatenate((dx.flatten(0), dy.flatten(0), dz.flatten(0))).reshape(3,dx.shape[0]*dx.shape[1]))
XYZp = RGam * RDel * RNu * M
xp = dnp.array(XYZp[0]).reshape(dx.shape)
yp = dnp.array(XYZp[1]).reshape(dy.shape)
zp = dnp.array(XYZp[2]).reshape(dz.shape)
# don't bother with the part about slits...
# Calculate effective gamma and delta for each pixel
d_ds = dnp.sqrt(xp**2 + yp**2 + zp**2)
Gam = dnp.arctan2(zp, yp)
Del = -1 * dnp.arcsin(-xp/d_ds)
# wavenumber
k = 2 * math.pi / 12.398 * energy
# Define the needed matrices. The notation follows the article by Bunk &
# Nielsen. J.Appl.Cryst. (2004) 37, 216-222.
M1 = k * numpy.matrix(cos(omega) * sin(Del) - sin(omega) * (cos(alpha) * (cos(Gam) * cos(Del)-1) + sin(alpha) * sin(Gam) * cos(Del)))
M2 = k * numpy.matrix(sin(omega) * sin(Del) + cos(omega) * (cos(alpha) * (cos(Gam) * cos(Del)-1) + sin(alpha) * sin(Gam) * cos(Del)))
M3 = k * numpy.matrix(-sin(alpha) * (cos(Gam) * cos(Del)-1) + cos(alpha) * sin(Gam) * cos(Del))
# invert UB matrix
UBi = numpy.matrix(UB).getI()
# calculate HKL
H = UBi[0,0]*M1 + UBi[0,1]*M2 + UBi[0,2]*M3
K = UBi[1,0]*M1 + UBi[1,1]*M2 + UBi[1,2]*M3
L = UBi[2,0]*M1 + UBi[2,1]*M2 + UBi[2,2]*M3
return (H, K, L)
def refl(runfiles,
pathtofiles,
outputpath,
scalar,
beamheight,
footprint,
angularfudgefactor,
wl,
back=()):
# scalar - scale factor to divide the data by
# beamheight FWHM in microns
# footprint in mm
# angular offset correction in degrees
# wavelength in wl
# back is an optional variable to subtract a background, set back=1 to do a background subtraction
qq = []
RR = []
dR = []
ii = -1
for filename in runfiles:
data = dnp.io.load(pathtofiles + "/" + str(filename) + ".dat")
ii += 1
theta = data.alpha
# work out the q vector
qqtemp = 4 * dnp.pi * dnp.sin(
(theta + angularfudgefactor) * dnp.pi / 180) / wl
#qqtemp = data.qdcd
# this section is to allow users to set limits on the q range used from each file
if not 'qmin' + str(ii) in refl.__dict__:
qmin = qqtemp.min()
else:
print "USER SET",
qmin = refl.__getattribute__('qmin' + str(ii))
print 'refl.qmin' + str(ii) + " = " + str(qmin) + " ;",
if not 'qmax' + str(ii) in refl.__dict__:
qmax = qqtemp.max()
else:
print "USER SET",
qmax = refl.__getattribute__('qmax' + str(ii))
print 'refl.qmax' + str(ii) + " = " + str(qmax) + " ;",
roi1_sum = data.roi1_sum
roi1_sum = roi1_sum[dnp.where((qqtemp >= qmin) & (qqtemp <= qmax))]
roi1dr = dnp.sqrt(roi1_sum)
theta = theta[dnp.where((qqtemp >= qmin) & (qqtemp <= qmax))]
qqtemp = qqtemp[dnp.where((qqtemp >= qmin) & (qqtemp <= qmax))]
bg_sum = dnp.zeros(len(roi1_sum))
bg_dr = dnp.zeros(len(roi1dr))
# if background ROI number given as int, convert to a single-item tuple
if type(back) == int:
back = (back, )
# subtract any background ROIs from the data
if len(back) > 0 and back[0] > 0:
if ii == 0:
print "Using background from " + str(
len(back)) + " ROIs: " + str(back)
for bg in back:
if ('roi' + str(bg) + '_sum' in data.keys()):
bg_cur = data[data.keys().index('roi' + str(bg) + '_sum')]
dr_cur = dnp.sqrt(bg_cur)
(bg_sum, bg_dr) = ep.EPadd(bg_sum, bg_dr, bg_cur, dr_cur)
(bg_sum, bg_dr) = ep.EPmulk(bg_sum, bg_dr, 1.0 / len(back))
else:
if ii == 0:
print "Not subtracting a background"
(RRtemp, drtemp) = ep.EPsub(roi1_sum, roi1dr, bg_sum, bg_dr)
# do a footprint correction.
# assumes that the beam is gaussian in profile, with a FWHM of "beamheight".
# footprint of sample is measured in mm.
areamultiplier = 2 * (norm.cdf(
footprint * dnp.sin(
(theta + angularfudgefactor) / 180 * dnp.pi) / 2, 0,
1e-3 * beamheight / (2 * dnp.sqrt(2 * dnp.log(2)))) - 0.5)
RRtemp /= areamultiplier
drtemp /= areamultiplier
# for the 2nd, 3rd, 4th q ranges have to splice the data on the end of the preexisting data
if (ii > 0):
# splice
(scalingfactor,
sferror) = nsplice.getScalingInOverlap(qq, RR, dR, qqtemp, RRtemp,
drtemp)
RRtemp *= scalingfactor
drtemp *= scalingfactor
print "Error in scaling factor: %2.3f %%" % (sferror /
scalingfactor * 100)
# now concatenate the data.
qq = dnp.concatenate((qq, qqtemp))
RR = dnp.concatenate((RR, RRtemp))
dR = dnp.concatenate((dR, drtemp))
# end of per-file loop
RR /= scalar
dR /= scalar
RR = RR[np.argsort(qq)]
dR = dR[np.argsort(qq)]
qq = np.sort(qq)
# write out the data.
np.savetxt(
outputpath + "/" + str(runfiles[0]) + "_refl.dat",
dnp.concatenate((qq, RR, dR)).reshape(3, qq.shape[0]).transpose())
