def search_for_peaks (self, peaks, thr, max_peak_size, num_of_segments, perc):
#thr=self.threshold # 100: raw counts threshold for locating peaks
#max_peak_size=self.mindist # 10: max allowed peak size with pixels above local background + Imin
#num_of_segments = [self.pbox,self.pbox] # [50.,50.]: number of segments in X and Y for local labckground estimation
#perc=self.bbox # 1.0: percent of median for background
topX=self.imArraySize[0]
topY=self.imArraySize[1]
img1=cgd.congrid(self.imArray, [1000,1000])
bg=self.estimate_local_background (img1, 50, 50, 100, 1.0)
w=np.where(img1-bg > thr)
for i in range(len(w[0])):
XYs=[w[1][i],w[0][i]]
if img1[XYs[1],XYs[0]]-bg[XYs[1],XYs[0]] > thr :
XY=[0.0,0.0]
aa=self.grow_peak(img1, bg, XYs[1],XYs[0], thr/2., 1000, 1000)
if (max([aa[1]-aa[0], aa[3]-aa[2]]) < max_peak_size) and (aa[6] > thr) :
XY[0]=aa[5]*topX/1000
XY[1]=aa[4]*topY/1000
peak=myPeakTable.myPeak()
peak.setDetxy(XY)
#peak.setIntAD=img1[aa[4],aa[5]]
#ref_peak.setgonio=im.sts.gonio
peaks.addPeak(peak)
img1[aa[2]:aa[3],aa[0]:aa[1]]=0
peaks.find_multiple_peak_copies()
def generate_peaks_laue(self, ub, optable, pred, en, exti, DAC_open):
gonio = [0., 0., 0., 0., 0., 0.]
for h in range(pred.h1, pred.h2 + 1):
for k in range(pred.k1, pred.k2 + 1):
for l in range(pred.l1, pred.l2 + 1):
if (h == 0 and k == 0 and l == 0):
continue
hkl = np.asarray([h, k, l])
if (syst_extinction(exti, hkl) == 1):
xyz = np.dot(ub, hkl)
if (vlength(xyz) > 0.0000001):
#print 'hkl is : ', h,k,l
Ene = en_from_xyz(xyz)
if (Ene > en[0] and Ene <= en[1]):
pix = np.asarray(
self.calculate_pixels_from_xyz(xyz, gonio))
r = pix - np.asarray([self.beamx, self.beamy])
r = sqrt(r[0]**2 + r[1]**2)
psi2 = self.calculate_psi_angles(gonio, pix)
if pix[0] > 0 and pix[0] < self.nopixx-1 and \
pix[1]>0 and pix[1] < self.nopixy -1 and abs(psi2[0,1]) < DAC_open :
refpeak = myPeakTable.myPeak()
refpeak.DetXY = pix
refpeak.gonio = gonio
refpeak.XYZ = xyz
refpeak.energies[0] = Ene
refpeak.hkl = hkl
optable.addPeak(refpeak)
def generate_all_peaks (self, ub, pktable, wv, pred, exti, dac_open, box):
pktable.zero()
kt = self.read_kappa_and_ttheta()
gonio = np.zeros(6, dtype=np.float32)
# 2theta
gonio[1] = kt[1]
# kappa/chi
gonio[4] = pred.chi
boxval = self.topLevel.read_box_change()
refpeak = myPeakTable.myPeak()
refpeak.IntSSD[0:2]=[boxval, boxval]
for h in range (pred.h1, pred.h2+1) :
for k in range (pred.k1, pred.k2+1) :
for l in range (pred.l1, pred.l2+1) :
hkl = [h,k,l]
extinct = syst_extinction (exti, hkl)
if extinct == 1 :
xyz = np.dot (hkl,ub)
om = get_omega(A_to_kev(wv), xyz)
#om = solve_general_axis (A_to_kev(wv), xyz, gonio)
om0 = om[0]
if om0 >= pred.om_start and om0 <= pred.om_start + pred.om_range :
gonio[3] = om0
pix = self.calculate_pixels_from_xyz(xyz, gonio)
r = [pix[0]-self.beamx, pix[1]-self.beamy]
r = math.sqrt (r[0]**2+r[1]**2)
psi2 = self.calculate_psi_angles(gonio, pix)
print pix[0], psi2[0][1]
if (pix[0]>0 and pix[0] < self.nopixx-1 and abs(psi2[0,1]< dac_open)) :
refpeak.setDetxy (pix)
refpeak.gonio = gonio
refpeak.xyz = xyz
refpeak.hkl = hkl
refpeak.IntSSD[0:2] = [box[0], box[0]]
pktable.appendPeak (refpeak)
def search_for_peaks(self, peaks, thr, max_peak_size, num_of_segments,
perc):
#thr=self.threshold # 100: raw counts threshold for locating peaks
#max_peak_size=self.mindist # 10: max allowed peak size with pixels above local background + Imin
#num_of_segments = [self.pbox,self.pbox] # [50.,50.]: number of segments in X and Y for local labckground estimation
#perc=self.bbox # 1.0: percent of median for background
#need to add intssd, gonio and any other parameters for the peak.
topX = self.imArraySize[0]
topY = self.imArraySize[1]
img1 = cgd.congrid(self.imArray, [1000, 1000])
bg = self.estimate_local_background(img1, self.locBcgr, self.locBcgr,
100, 1.0)
w = np.where(img1 - bg > thr)
for i in range(len(w[0])):
XYs = [w[1][i], w[0][i]]
if img1[XYs[1], XYs[0]] - bg[XYs[1], XYs[0]] > thr:
XY = [0.0, 0.0]
aa = self.grow_peak(img1, bg, XYs[1], XYs[0], thr / 2., 1000,
1000)
if (max([aa[1] - aa[0], aa[3] - aa[2]]) <
max_peak_size) and (aa[6] > thr):
XY[0] = aa[5] * topX / 1000
XY[1] = aa[4] * topY / 1000
peak = myPeakTable.myPeak()
peak.setDetxy(XY)
#peak.setIntAD=img1[aa[4],aa[5]]
#ref_peak.setgonio=im.sts.gonio
peaks.addPeak(peak)
img1[aa[2]:aa[3], aa[0]:aa[1]] = 0
peaks.find_multiple_peak_copies()
def search_for_peaks_arr(self, arr, peaks, thr, max_peak_size,
num_of_segments, perc):
sxy = arr.shape
topX = sxy[1]
topY = sxy[0]
img1 = cgd.congrid(arr, [1000, 1000])
bg = self.estimate_local_background(img1, self.locBcgr, self.locBcgr,
100, 1.0)
w = np.where(img1 - bg > thr)
for i in range(len(w[0])):
XYs = [w[1][i], w[0][i]]
if img1[XYs[1], XYs[0]] - bg[XYs[1], XYs[0]] > thr:
XY = [0.0, 0.0]
aa = self.grow_peak(img1, bg, XYs[1], XYs[0], thr / 2., 1000,
1000)
if (max([aa[1] - aa[0], aa[3] - aa[2]]) <
max_peak_size) and (aa[6] > thr):
XY[0] = aa[5] * topX / 1000
XY[1] = aa[4] * topY / 1000
peak = myPeakTable.myPeak()
peak.setDetxy(XY)
#peak.setIntAD=img1[aa[4],aa[5]]
#ref_peak.setgonio=im.sts.gonio
peaks.addPeak(peak)
img1[aa[2]:aa[3], aa[0]:aa[1]] = 0
peaks.find_multiple_peak_copies()
def generate_all_peaks (self, ub, pktable, wv, pred, exti, dac_open, box):
pktable.zero()
kt = self.read_kappa_and_ttheta()
gonio = np.zeros(6, dtype=np.float32)
# 2theta
gonio[1] = kt[1]
# kappa/chi
gonio[4] = pred.chi
boxval = self.topLevel.read_box_change()
refpeak = myPeakTable.myPeak()
refpeak.IntSSD[0:2]=[boxval, boxval]
for h in range (pred.h1, pred.h2+1) :
for k in range (pred.k1, pred.k2+1) :
for l in range (pred.l1, pred.l2+1) :
hkl = [h,k,l]
extinct = syst_extinction (exti, hkl)
if extinct == 1 :
xyz = np.dot (hkl,ub)
om = get_omega(A_to_kev(wv), xyz)
#om = solve_general_axis (A_to_kev(wv), xyz, gonio)
om0 = om[0]
if om0 >= pred.om_start and om0 <= pred.om_start + pred.om_range :
gonio[3] = om0
pix = self.calculate_pixels_from_xyz(xyz, gonio)
r = [pix[0]-self.beamx, pix[1]-self.beamy]
r = math.sqrt (r[0]**2+r[1]**2)
psi2 = self.calculate_psi_angles(gonio, pix)
print pix[0], psi2[0][1]
if (pix[0]>0 and pix[0] < self.nopixx-1 and abs(psi2[0,1]< dac_open)) :
refpeak.setDetxy (pix)
refpeak.gonio = gonio
refpeak.xyz = xyz
refpeak.hkl = hkl
refpeak.IntSSD[0:2] = [box[0], box[0]]
pktable.appendPeak (refpeak)
def generate_peaks_laue (self, ub, optable, pred, en, exti, DAC_open):
gonio = [0.,0.,0.,0.,0.,0.]
for h in range(pred.h1,pred.h2+1) :
for k in range (pred.k1, pred.k2+1) :
for l in range (pred.l1, pred.l2+1) :
if (h==0 and k==0 and l==0) :
continue
hkl =np.asarray([h, k, l])
if (syst_extinction (exti, hkl)==1) :
xyz = np.dot (ub, hkl)
if (vlength(xyz) >0.0000001) :
#print 'hkl is : ', h,k,l
Ene = en_from_xyz(xyz)
if (Ene > en[0] and Ene <= en[1]) :
pix = np.asarray(self.calculate_pixels_from_xyz (xyz, gonio))
r = pix - np.asarray([self.beamx, self.beamy])
r = sqrt(r[0]**2+r[1]**2)
psi2 = self.calculate_psi_angles (gonio, pix)
if pix[0] > 0 and pix[0] < self.nopixx-1 and \
pix[1]>0 and pix[1] < self.nopixy -1 and abs(psi2[0,1]) < DAC_open :
refpeak = myPeakTable.myPeak()
refpeak.DetXY = pix
refpeak.gonio = gonio
refpeak.XYZ = xyz
refpeak.energies[0] =Ene
refpeak.hkl = hkl
optable.addPeak (refpeak)
def search_for_peaks_arr (self, arr, peaks, thr, max_peak_size, num_of_segments, perc):
sxy = arr.shape
topX=sxy[1]
topY=sxy[0]
img1=cgd.congrid(arr, [1000,1000])
bg=self.estimate_local_background (img1, 50, 50, 100, 1.0)
w=np.where(img1-bg > thr)
for i in range(len(w[0])):
XYs=[w[1][i],w[0][i]]
if img1[XYs[1],XYs[0]]-bg[XYs[1],XYs[0]] > thr :
XY=[0.0,0.0]
aa=self.grow_peak(img1, bg, XYs[1],XYs[0], thr/2., 1000, 1000)
if (max([aa[1]-aa[0], aa[3]-aa[2]]) < max_peak_size) and (aa[6] > thr) :
XY[0]=aa[5]*topX/1000
XY[1]=aa[4]*topY/1000
peak=myPeakTable.myPeak()
peak.setDetxy(XY)
#peak.setIntAD=img1[aa[4],aa[5]]
#ref_peak.setgonio=im.sts.gonio
peaks.addPeak(peak)
img1[aa[2]:aa[3],aa[0]:aa[1]]=0
peaks.find_multiple_peak_copies()
