def analyzeCDM_motion(rNo, procList):
""" load data and images """
procFile = []
set = 2
for ii in range(len(rNo)):
str = '%d.dat' % rNo[ii]
procFile.append([s for s in procList if str in s])
str = ''.join(procFile[ii])
df = pd.read_table(str, usecols=[0, 1, 9, 20])
if not 'imgs' in locals():
imgs = fem.getPilatusImgs(df, rNo[ii], set)
else:
imgs = imgs + fem.getPilatusImgs(df, rNo[ii], set)
""" calculate angles for each pixels of each images """
E = 7
alpha = 0.5
pilH = 2.75 # [mm]
pilV = -47.5 # [mm]
xdb = 21.6 - 414. * 0.170 # [mm]
ydb = -58.03 - 188. * 0.17 # [mm]
d = 74
img_range = [12, 10]
delta, gamma, images = fem.getPixelsAngles(imgs, pilH, pilV, xdb, ydb, d,
img_range)
E = 7
alpha = 0.5
N = np.array([-1, -1, 2])
omega = -63.66
omega_offset = 0 # offset with respect to the UB matrix
rot = np.array(df['# top rotation']) - omega_offset
a = np.array([5.39, 5.40, 7.61])
aa = np.array([90, 90, 90.07])
U, B = tldiff.UBmat(a, aa, N)
""" find center of mass of the peak """
CDMidx, CDMmaxidx = tlimg.centerOfMass(images)
idxup = np.ceil(CDMidx)
idxdown = np.floor(CDMidx)
CDM_omega = np.interp(CDMidx[0], np.array([idxdown[0], idxup[0]]),
np.array([rot[idxdown[0]], rot[idxup[0]]]))
omegaMax = rot[CDMmaxidx[0]]
CDM_delta = np.interp(
CDMidx[0], np.array([idxdown[0], idxup[0]]),
np.array([delta[idxdown[1], idxdown[2]], delta[idxup[1], idxup[2]]]))
CDM_gamma = np.interp(
CDMidx[0], np.array([idxdown[0], idxup[0]]),
np.array([gamma[idxdown[1], idxdown[2]], gamma[idxup[1], idxup[2]]]))
CDMhkl, CDMQ = tldiff.hklFromAngles(E, CDM_delta, CDM_gamma, CDM_omega,
alpha, U, B)
images = np.sum(images, axis=0)
return images, CDMhkl, CDM_omega, CDM_delta, CDM_gamma, procFile, omegaMax
sys.path.append('Z:/Data1')
procList = glob.glob('Z:\Data1\*\proc\proc*.dat')
procFile = []
dfList = []
set = 2
count = 0
for ii in range(rNo_all.shape[0]):
for jj in range(rNo_all.shape[1]):
rNo = rNo_all[ii, jj]
string = '%d.dat' % rNo
procFile.append([s for s in procList if string in s])
string = ''.join(procFile[count])
temp = pd.read_table(string, usecols=[0, 1, 9, 20, 39])
dfList.append(temp)
imgs_temp, uimgs_temp = fem.getPilatusImgs(dfList[count], set)
imgs_all[ii, :, :, :] = imgs_all[ii, :, :, :] + imgs_temp
uimgs_all[ii, :, :, :] = uimgs_all[ii, :, :, :] + uimgs_temp
count += 1
del (imgs_temp)
del (uimgs_temp)
peakPix = [155, 92] # pixels position of the peak [x,y]
roi = np.array([[peakPix[0] - 22, peakPix[0] + 22],
[peakPix[1] - 20,
peakPix[1] + 20]]) # [[xmin,xmax][ymin,ymax]]
imgs_all = imgs_all[:, :, roi[1][0]:roi[1][1],
roi[0][0]:roi[0][1]] # line = y, column = x
uimgs_all = uimgs_all[:, :, roi[1][0]:roi[1][1],
roi[0][0]:roi[0][1]] # line = y, column = x
#d = 74 # distance sample - detector [mm]
if not 'data_pilgate' in locals():
data_pilgate = dict()
data_pilgate_ave = dict()
count = 0
for ii in range(len(rNo_pilgate)):
print('pilgate ii = %d' % ii)
data_pilgate[ii] = pd.DataFrame()
for jj in range(rNo_pilgate[ii].size):
rNo = rNo_pilgate[ii][jj]
string = '%d.dat' %rNo
procFile_pilgate.append( [s for s in procList if string in s] )
string = ''.join(procFile_pilgate[count])
temp = pd.read_table(string,usecols=[0,1,9,18,20])
imgs_temp, uimgs_temp = fem.getPilatusImgs(temp, set)
int_roi = np.zeros(imgs_temp.shape[0])
int_bkg = np.zeros(imgs_temp.shape[0])
total_int = np.zeros(131)
for kk in range(imgs_temp.shape[0]):
# roi = [[0,178],[0,212]] # [[xmin, xmax], [ymin, ymax]]
roi = [[94-20,94+20],[165-25,165+25]] # [[xmin, xmax], [ymin, ymax]]
# note: the import routine create an array from the images, which invert the axis
bkgroi = np.add(roi , [[-30,30],[-30,30]] )
temp_int_roi, temp_int_bkg = imgana.roi_bkgRoi(imgs_temp[kk], roi, bkgroi)
int_roi[kk] = temp_int_roi
int_bkg[kk] = temp_int_bkg
# if kk % 10 == 0: