def eps_eq_core(a1, a2, eps, out, prefix):
if (hasattr(a1, "__len__")): # traverse list
assert len(a1) == len(a2)
for i in xrange(len(a1)):
if (not eps_eq_core(a1[i], a2[i], eps, out, prefix+" ")):
return False
return True
if (isinstance(a1, complex)): # complex numbers
if (not eps_eq_core(a1.real, a2.real, eps, out, prefix+"real ")):
return False
if (not eps_eq_core(a1.imag, a2.imag, eps, out, prefix+"imag ")):
return False
return True
ok = True
if (a1 == 0 or a2 == 0):
if (abs(a1-a2) > eps):
ok = False
else:
l1 = round(math.log(abs(a1)))
l2 = round(math.log(abs(a2)))
m = math.exp(-max(l1, l2))
if (abs(a1*m-a2*m) > eps):
ok = False
if (out is not None):
annotation = ""
if (not ok):
annotation = " eps_eq ERROR"
print >> out, prefix + str(a1) + annotation
print >> out, prefix + str(a2) + annotation
print >> out, prefix.rstrip()
return True
return ok
def eps_eq_core(a1, a2, eps, out, prefix):
if (hasattr(a1, "__len__")): # traverse list
assert len(a1) == len(a2)
for i in xrange(len(a1)):
if (not eps_eq_core(a1[i], a2[i], eps, out, prefix+" ")):
return False
return True
if (isinstance(a1, complex)): # complex numbers
if (not eps_eq_core(a1.real, a2.real, eps, out, prefix+"real ")):
return False
if (not eps_eq_core(a1.imag, a2.imag, eps, out, prefix+"imag ")):
return False
return True
ok = True
if (a1 == 0 or a2 == 0):
if (abs(a1-a2) > eps):
ok = False
else:
l1 = round(math.log(abs(a1)))
l2 = round(math.log(abs(a2)))
m = math.exp(-max(l1, l2))
if (abs(a1*m-a2*m) > eps):
ok = False
if (out is not None):
annotation = ""
if (not ok):
annotation = " eps_eq ERROR"
print >> out, prefix + str(a1) + annotation
print >> out, prefix + str(a2) + annotation
print >> out, prefix.rstrip()
return True
return ok
def preprocess_image(h5file, flag):
f = h5py.File(h5file, 'r')
f.keys()
file = f['trans_data'][:]
f.close()
p_list = []
for i in range(0, 11):
for j in range(0, 11):
value = file[i][j]
p_list.append(value)
v_min = 0
p_max = max(p_list)
p_min = min(p_list)
print p_max, smath.log((float(p_max)))
if p_min > float(0):
add_value = 0
else:
add_value = smath.minus(0, p_min) + 1
print "==add_value==", add_value
image = flex.vec3_double()
original = open(h5file.split(".")[0] + '_original.dat', 'w')
for x in range(0, 11):
for y in range(0, 11):
if flag == 0:
value = file[x][y]
elif flag == 1:
value = smath.log(
(float(file[x][y]) + add_value)
) #lg value is too small...4 5 6 7...cannot be recognized
elif flag == 2:
v_max = 255
print "=------v_max------=", v_max
value = round((v_max - v_min) * (file[x][y] - p_min) /
(p_max - p_min) + v_min)
else:
v_max = int(max(p_list) / 10)
print "=------v_max------=", v_max
value = round((v_max - v_min) * (file[x][y] - p_min) /
(p_max - p_min) + v_min)
image.append([x, y, value])
print >> original, x, y, value
original.close()
return image
def q2(self,rmax):
a = 3.019
b = -1.28945
x = float(rmax)
if x<10:
x = 10.0
if x > 200:
x = 200.0
x = smath.log(x)
y = a+b*x
return smath.exp(y)
def q1(self,rmax):
a=-3.724
b=1.269
c=-0.2639
x = float(rmax)
if x<10:
x = 10.0
if x > 200:
x = 200.0
x = smath.log(x)
y = a+b*x+c*x*x
return smath.exp(y)
def precision_approx_equal(self,other,precision=24):
# Use concepts from IEEE-754 to determine if the difference between
# two numbers is within floating point error. Precision is expressed in
# bits (single precision=24; double precision=53). Not within scope to
# do this for double precision literals; only interested in the case
# where the data are from a ~single precision digital-analog converter.
if precision > 52: raise ValueError()
if self==other:
return True
if (self > 0.) != (other > 0.) :
return False
#compute the exponent
import math
T = abs(self)
Np = math.floor(precision-math.log(T,2))
significand = int(T * 2**Np)
val1 = significand/(2**Np) # nearest floating point representation of self
val2 = (significand+1)/(2**Np) # next-nearest
return abs(T-abs(other)) <= abs(val1-val2)
def precision_approx_equal(self,other,precision=24):
# Use concepts from IEEE-754 to determine if the difference between
# two numbers is within floating point error. Precision is expressed in
# bits (single precision=24; double precision=53). Not within scope to
# do this for double precision literals; only interested in the case
# where the data are from a ~single precision digital-analog converter.
if precision > 52: raise ValueError()
if self==other:
return True
if (self > 0.) != (other > 0.) :
return False
#compute the exponent
import math
T = abs(self)
Np = math.floor(precision-math.log(T,2))
significand = int(T * 2**Np)
val1 = significand/(2**Np) # nearest floating point representation of self
val2 = (significand+1)/(2**Np) # next-nearest
return abs(T-abs(other)) <= abs(val1-val2)
def tst_image(n, N=100, filename=None):
nmax = n
nmax0 = 8
nl_array0 = math.nl_array(nmax0)
nls0 = nl_array0.nl()
nl_array = math.nl_array(nmax)
nls = nl_array.nl()
if (filename is not None):
image = read_data(filename)
else:
moments = flex.random_double(nls0.size())
# moments=flex.double(nls.size(),0 )
# moments[3] = 1.0
# moments[7] = 1.0
image = generate_image(n, moments)
orig = open('original.dat', 'w')
for pt in image:
print >> orig, pt[0], pt[1], pt[2]
orig.close()
Nq = 100
Nphi = 100
c2_image = from_I_to_C2(nmax, image, Nq, Nphi)
c2_output = open('c2.dat', 'w')
for pt in c2_image:
print >> c2_output, pt[0], pt[1], pt[2]
c2_output.close()
coef_table = calc_integral_triple_zernike2d(nmax)
moments[0] = 0
new_mom = moments.concatenate(flex.double(nls.size() - nls0.size(), 0))
nmax4c2 = 2 * nmax
Inm = math.nl_c_array(nmax4c2)
Inm.load_coefs(nls, new_mom)
cnm = calc_Cnm_from_Inm(Inm, coef_table, nmax4c2)
cnm_coef = cnm.coefs()
print "#cnm[0]", cnm_coef[0]
# cnm_coef[0]=0
### calculate 2d zernike moments for C2_image ###
image = c2_image
NP = int(smath.sqrt(image.size()))
N = NP / 2
grid_2d = math.two_d_grid(N, nmax)
grid_2d.clean_space(image)
grid_2d.construct_space_sum()
zernike_2d_mom = math.two_d_zernike_moments(grid_2d, nmax)
c2_moments = zernike_2d_mom.moments()
#coefs = flex.real( c2_moments )
#cnm_coef = flex.real( c2_moments )
cnm_coef = c2_moments
c2_reconst = generate_image2(n, cnm_coef, c2_image, Nq)
c2_r = open('c2r.dat', 'w')
for pt in c2_reconst:
print >> c2_r, pt[0], pt[1], pt[2]
c2_r.close()
ls_score = 0
np_tot = c2_image.size()
for p1, p2 in zip(c2_image, c2_reconst):
ls_score += (p1[2] - p2[2])**2.0
print nmax, nls.size(), ls_score, np_tot * smath.log(
ls_score / np_tot), "SUM"
for nl, c2m in zip(nls, cnm_coef):
print nl, c2m
exit() #
### calculate 2d zernike moments for C2_image ###
image = c2_image
NP = int(smath.sqrt(image.size()))
N = NP / 2
grid_2d = math.two_d_grid(N, nmax)
grid_2d.clean_space(image)
grid_2d.construct_space_sum()
tt2 = time.time()
zernike_2d_mom = math.two_d_zernike_moments(grid_2d, nmax)
c2_moments = zernike_2d_mom.moments()
#coefs = flex.real( c2_moments )
coefs = (c2_moments)
for nl, c2m, c2mm in zip(nls, cnm_coef, coefs):
if (nl[0] / 2 * 2 == nl[0]):
print c2m, c2mm
coefs = flex.real(moments)
nl_array.load_coefs(nls, coefs)
for nl, c in zip(nls, moments):
if (abs(c) < 1e-3):
c = 0
print nl, c
print
reconst = flex.complex_double(NP**2, 0)
for nl, c in zip(nls, moments):
n = nl[0]
l = nl[1]
if (l > 0):
c = c * 2.0
rap = math.zernike_2d_polynome(n, l)
i = 0
for x in range(0, NP):
x = x - N
for y in range(0, NP):
y = y - N
rr = smath.sqrt(x * x + y * y) / N
if rr > 1.0:
value = 0.0
else:
tt = smath.atan2(y, x)
value = rap.f(rr, tt)
reconst[i] = reconst[i] + value * c
i = i + 1
rebuilt = open('rebuilt.dat', 'w')
i = 0
for x in range(0, NP):
for y in range(0, NP):
value = reconst[i].real
print >> rebuilt, x, y, image[i][2], value
i = i + 1
rebuilt.close()
def preprocess_image(h5file):
f = h5py.File(h5file,'r')
f.keys()
file = f['trans_data'][:]
f.close()
p_list=[]
for i in range(0, 11):
for j in range(0, 11):
value = file[i][j]
p_list.append(value)
v_min=0
p_max=max(p_list)
p_min=min(p_list)
print p_max,smath.log((float(p_max)))
if p_min>float(0):
add_value = 0
else:
add_value = smath.minus(0, p_min)+1
image = flex.vec3_double()
original = open(h5file.split(".")[0]+'_original.dat','w')
for x in range(0, 11):
for y in range(0, 11):
value = file[x][y]
image.append([x,y,value])
print>>original, x,y, value
original.close()
return image
def tst_2d_zernike_mom(n, l, file):
# image = preprocess_image(h5file)
image = ImageToDat(file)
feature_maxtix = []
NP=int(smath.sqrt( image.size() ))
N=int(NP/2)
# print"=====",NP, N
grid_2d = math.two_d_grid(N, nmax)
grid_2d.clean_space( image )
grid_2d.construct_space_sum()
zernike_2d_mom = math.two_d_zernike_moments( grid_2d, nmax )
moments = zernike_2d_mom.moments()
coefs = flex.real( moments )
nl_array = math.nl_array( nmax )
nls = nl_array.nl()
# nl_array.load_coefs( nls, coefs )
# lfg = math.log_factorial_generator(nmax)
for nl, c in zip( nls, moments):
if(abs(c)<1e-3):
c=0
feature_maxtix.append(c.real)
# print nl,c
return feature_maxtix
def DatToMatrix(filename):
f = open(filename)
# res = np.zeros((500,500), dtype=np.float)
res = np.zeros((11, 11), dtype=np.float)
for line in f.readlines():
data = line.split(" ")
x=int(data[0])
y=int(data[1])
value=float(data[2])
res[x][y] = value
f.close()
return res
def MatrixToImage(data):
data =data*float(.1) #to see the shape clearly...
new_im = Image.fromarray(data)
return new_im
def H5ToMatrix(filename):
f = h5py.File(filename,'r')
f.keys()
file = f['trans_data'][:]
return file
def plot_h5(filename):
data = H5ToMatrix(filename)
im = MatrixToImage(data)
im.show()
if __name__ == "__main__":
args = sys.argv[1:]
cluster_num = args[0]
t_s = time.time()
n = 2
l = 2
nmax = max(5, n)
# create a list of images
path = '/Users/wyf/Documents/SFX/NoGap'
# path = '/Users/wyf/Documents/crop_image'
# make sure the list is in order,1,2,3,...11,...99,100...1000,1001... since matching node's id depends on the files' order
files = os.listdir(path)
files.remove('.DS_Store')
files.sort(key = lambda x: int(x.split(".")[0].split("transfered")[1]))
h5_list = [os.path.join(path, f) for f in files if f.endswith('7.h5')]
# imlist=[]
# for p in range(len(h5_list)):
# # pylab.subplot(4, 5, p + 1)
# h5name = h5_list[p]
# mat = H5ToMatrix(h5name)
# im = MatrixToImage(mat)
# imlist.append(im)
t1 = time.time()
features = np.zeros([len(h5_list), 12], dtype=np.float)
# when namx=5, a vec has 21 items, but the real part is the same when l<0, so just calculate l>=0, 12 items.
for i, f in enumerate(h5_list):
features[i] = tst_2d_zernike_mom(n, l, f)
# print "features Matrix:"
# print features
t2 = time.time()
print "time used for getting features matrix: ", t2 - t1
t3 = time.time()
tree_list = Clustering.hcluster(cluster_num, features)
# tree_list_plot = Clustering.hcluster(cluster_num, features)
t4 = time.time()
print "time used for clustering: ", t4 - t3
# input outcome
print "cluster outcome:"
t5 = time.time()
res = open(str(cluster_num)+'_ac_res.dat','w')
while (len(tree_list)>0):
for root in tree_list:
print root.get_cluster_elements()
print>>res, root.get_cluster_elements()
tree_list.remove(root)
t6 = time.time()
# print "time used for generating result: ", t6 - t5
# for i in range(len(tree_list_plot)):
# if len(tree_list_plot[i].get_cluster_elements())!=1:
# clusters = tree_list_plot[i].extract_clusters(0.2 * tree_list_plot[i].distance)
# Clustering.draw_dendrogram(tree_list_plot[i],imlist,filename='sunset.pdf')
# else:
# im.show(imlist[i])
t_e = time.time()
print "total time used: ", t_e - t_s
print "OK"
def write_saxs_image(self, file_name):
pp= open(file_name,'w')
for ii in range(self.np):
for jj in range(self.np):
print >> pp, ii, jj, smath.log( self.saxs_image[ (ii,jj) ]+1e-12 )
print >> pp
def write_scattering_pattern(data,np,outobj):
for ii in range(np):
for jj in range(np):
print >> outobj, ii,jj,smath.log(data[(ii,jj)] )
print >> outobj