def mean_var_xy(xy, rx=10, ry=10):
int_x, int_y = int(round(xy[0])), int(round(xy[1]))
xys = []
for i in range(int_x - rx, int_x + rx + 1):
for j in range(int_y - ry, int_y + ry + 1):
xys.append((i, j))
xys = tuple(xys)
subimages = np.zeros((100, len(xys)))
dirpath = "/home/fengguo/Data/Si2g_200N/nomvt/"
for i in range(100):
path = dirpath + "S2gnomvt_%s_mar.tiff" % stringint(i, 4)
f = open(path, "rb")
f.seek(offset)
image = np.fromfile(f, dtype=formatdata, count=nb_elem).reshape(framedim)
for j in range(len(xys)):
subimages[i, j] = image[xys[j]] + bkg[xys[j]] - 100
f.close()
mean, std = np.zeros(len(xys)), np.zeros(len(xys))
for i in range(len(xys)):
mean[i], std[i] = subimages[:, i].mean(), subimages[:, i].std()
var = std ** 2
return mean, var
xys = peaks[:, 0:2]
xykb = np.zeros((peaks.shape[0], 4))
for n in range(xys.shape[0]): # xys.shape[0]):
pixels = []
int_x, int_y = int(round(xys[n, 1])), int(round(xys[n, 0]))
for i in range(int_x - rx, int_x + rx + 1):
for j in range(int_y - ry, int_y + ry + 1):
pixels.append((i, j))
pixels = tuple(pixels)
subimages = np.zeros((100, len(pixels)))
# dirpath = '/home/fengguo/Data/Si2g_200N/nomvt/'
for i in range(100):
path = dirpath + "S2gnomvt_%s_mar.tiff" % stringint(i, 4)
f = open(path, "rb")
f.seek(offset)
image = np.fromfile(f, dtype=formatdata, count=nb_elem).reshape(framedim)
for j in range(len(pixels)):
subimages[i, j] = image[pixels[j]] + bkg[pixels[j]] - 100
f.close()
mean, std = np.zeros(subimages.shape[1]), np.zeros(subimages.shape[1])
for i in range(subimages.shape[1]):
mean[i], std[i] = subimages[:, i].mean(), subimages[:, i].std()
var = std ** 2
k, b = linregress(mean, var)[0:2]
xykb[n] = xys[n, 0], xys[n, 1], k, b
np.save("xykb", xykb)
# mean = images[:]
xsbl = image[xs - 1, ys:ye].max() > thre
ysbl = image[xs:xe, ys - 1].max() > thre
xebl = image[xe + 1, ys:ye].max() > thre
yebl = image[xs:xe, ye + 1].max() > thre
if xsbl:
xs = xs - 1
if ysbl:
ys = ys - 1
if xebl:
xe = xe + 1
if yebl:
ye = ye + 1
pk_bound[i] = xi, yi, xs, ys, xe, ye
mean, std = np.zeros((xe - xs, ye - ys)), np.zeros((xe - xs, ye - ys))
images = np.zeros((100, xe - xs, ye - ys))
for j in range(100):
path = dirpath + 'S1gnomvt_%s_mar.tiff' % stringint(j, 4)
f = open(path, 'rb')
f.seek(offset)
images[j] = np.fromfile(f, dtype=formatdata, count=nb_elem).reshape(
framedim)[xs:xe, ys:ye] + bkg[xs:xe, ys:ye] - 100
f.close()
for k in range(xe - xs):
for l in range(ye - ys):
mean[k, l], std[k, l] = images[:, k,
l].mean(), images[:, k,
l].std()
np.save('mean%i' % i, mean)
np.save('std%i' % i, std)
np.save('peak_bound', pk_bound)
xsbl, ysbl, xebl, yebl = True, True, True, True
while xsbl or ysbl or xebl or yebl:
xsbl = image[xs-1, ys:ye].max() > thre
ysbl = image[xs:xe, ys-1].max() > thre
xebl = image[xe+1, ys:ye].max() > thre
yebl = image[xs:xe, ye+1].max() > thre
if xsbl:
xs = xs-1
if ysbl:
ys = ys-1
if xebl:
xe = xe+1
if yebl:
ye = ye+1
pk_bound[i] = xi, yi, xs, ys, xe, ye
mean, std = np.zeros((xe-xs,ye-ys)), np.zeros((xe-xs,ye-ys))
images = np.zeros((100,xe-xs,ye-ys))
for j in range(100):
path = dirpath + 'S1gnomvt_%s_mar.tiff'%stringint(j, 4)
f = open(path, 'rb')
f.seek(offset)
images[j] = np.fromfile(f, dtype = formatdata, count = nb_elem).reshape(framedim)[xs:xe, ys:ye]+bkg[xs:xe, ys:ye]-100
f.close()
for k in range(xe-xs):
for l in range(ye-ys):
mean[k, l], std[k, l] = images[:, k, l].mean(), images[:, k, l].std()
np.save('mean%i'%i,mean)
np.save('std%i'%i,std)
np.save('peak_bound',pk_bound)
print i
f_cur, f_ref = open('%i200'%i,'w'),open('%iref'%i,'w')
f_cur.write('%i %i %i'%(len(mch),0,len(mch)))
f_ref.write('%i %i %i'%(len(mch),0,len(mch)))
for j in range(len(mch)):
f_cur.write('\n%i %i %f %f'%(j+1,0,cur[mch[j,1],0],-cur[mch[j,1],1]))
f_ref.write('\n%i %i %f %f'%(j+1,0,ref[mch[j,0],0],-ref[mch[j,0],1]))
f_cur.close()
f_ref.close()
"""
dd5, xcen5, ycen5, beta5, gamma5 = 59.836, 1365.74, 943.05, 0.373, 0.504
dd200, xcen200, ycen200, beta200, gamma200 = 59.836, 1365.74, 943.05, 0.373, 0.504
for n in range(0,1):
ref = np.load('pos%s_5.npy'%stringint(n,4))
cur = np.load('pos%s_50.npy'%stringint(n+7,4))
k_ref, k_cur = np.zeros((ref.shape[0], 3)), np.zeros((cur.shape[0], 3))
det2lab_mat5 = Det_Lab_Mat(beta5, gamma5)
for i in range(ref.shape[0]):
k_ref[i] = Detector_to_Lab(ref[i], det2lab_mat5, dd = dd5, xcen_pix = xcen5, ycen_pix = ycen5, pix_size = pix_size)
det2lab_mat200 = Det_Lab_Mat(beta200, gamma200)
for i in range(cur.shape[0]):
k_cur[i] = Detector_to_Lab(cur[i], det2lab_mat200, dd = dd200, xcen_pix = xcen200, ycen_pix = ycen200, pix_size = pix_size)
mch = np.array(Trace(k_ref, k_cur).values())
f_cur, f_ref = open('%i200'%n,'w'),open('%iref'%n,'w')
print len(mch)
f_cur.write('%i %i %i'%(len(mch),0,len(mch)))
f_ref.write('%i %i %i'%(len(mch),0,len(mch)))
for j in range(len(mch)):
import numpy as np
import finding as FIND
from common import stringint
offset = 4096
bkg_offset = 110
framedim = (2594, 2774)
nb_elem = framedim[0]*framedim[1]
formatdata = np.uint16
bkg_path = os.path.join('/', 'home', 'fengguo', 'Data', 'back_5s_0001.tif')
ref_path = '/home/fengguo/Data/Si1g_5N/scan/' + 'S1gscan_0440_mar.tiff'
path = '/home/fengguo/Data/Si1g_200N/scan/' + 'S2gscan_0001_mar.tiff'
bkg_f = open(bkg_path, 'rb')
bkg_f.seek(bkg_offset)
bkg = np.fromfile(bkg_f, dtype = formatdata, count = nb_elem).reshape(framedim)
bkg_f.close()
for i in range(0, 881):
path = '/home/fengguo/Data/Si1g_200N/scan/' + 'S2gscan_%s_mar.tiff'%stringint(4,i)
f = open(path, 'rb')
f.seek(offset)
image = np.fromfile(f, dtype = formatdata, count = nb_elem).reshape(framedim)
raw_image = image+bkg-100
pos=FindingPosition(raw_image, xdim = 2594, ydim = 2774)
np.save('pos%s'%stringint(4,i),pos)