def reference4(ref_data):
from utilities import print_msg
from filter import fit_tanh, filt_tanl, filt_gaussl
from fundamentals import fshift, fft
from morphology import threshold
# Prepare the reference in 3D alignment, i.e., low-pass filter and center.
# Input: list ref_data
# 0 - mask
# 1 - center flag
# 2 - raw average
# 3 - fsc result
# Output: filtered, centered, and masked reference image
# apply filtration (FSC) to reference image:
#print_msg("reference4\n")
cs = [0.0] * 3
stat = Util.infomask(ref_data[2], ref_data[0], False)
volf = ref_data[2] - stat[0]
Util.mul_scalar(volf, 1.0 / stat[1])
volf = threshold(volf)
#Util.mul_img(volf, ref_data[0])
#fl, aa = fit_tanh(ref_data[3])
fl = 0.25
aa = 0.1
#msg = "Tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n"%(fl, aa)
#print_msg(msg)
volf = fft(filt_gaussl(filt_tanl(fft(volf), 0.35, 0.2), 0.3))
if ref_data[1] == 1:
cs = volf.phase_cog()
msg = "Center x = %10.3f Center y = %10.3f Center z = %10.3f\n" % (
cs[0], cs[1], cs[2])
print_msg(msg)
volf = fshift(volf, -cs[0], -cs[1], -cs[2])
return volf, cs
def reference4( ref_data ):
from utilities import print_msg
from filter import fit_tanh, filt_tanl, filt_gaussl
from fundamentals import fshift, fft
from morphology import threshold
# Prepare the reference in 3D alignment, i.e., low-pass filter and center.
# Input: list ref_data
# 0 - mask
# 1 - center flag
# 2 - raw average
# 3 - fsc result
# Output: filtered, centered, and masked reference image
# apply filtration (FSC) to reference image:
#print_msg("reference4\n")
cs = [0.0]*3
stat = Util.infomask(ref_data[2], ref_data[0], False)
volf = ref_data[2] - stat[0]
Util.mul_scalar(volf, 1.0/stat[1])
volf = threshold(volf)
#Util.mul_img(volf, ref_data[0])
#fl, aa = fit_tanh(ref_data[3])
fl = 0.25
aa = 0.1
#msg = "Tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n"%(fl, aa)
#print_msg(msg)
volf = fft(filt_gaussl(filt_tanl(fft(volf),0.35,0.2),0.3))
if ref_data[1] == 1:
cs = volf.phase_cog()
msg = "Center x = %10.3f Center y = %10.3f Center z = %10.3f\n"%(cs[0], cs[1], cs[2])
print_msg(msg)
volf = fshift(volf, -cs[0], -cs[1], -cs[2])
return volf, cs
def compute_average(mlist, radius, CTF):
from morphology import cosinemask
from fundamentals import fft
from statistics import fsc, sum_oe
if CTF:
avge, avgo, ctf_2_sume, ctf_2_sumo, params_list = \
sum_oe(mlist, "a", CTF, None, True, True)
avge = cosinemask(fft(avge), radius)
avgo = cosinemask(fft(avgo), radius)
sumavge = Util.divn_img(fft(avge), ctf_2_sume)
sumavgo = Util.divn_img(fft(avgo), ctf_2_sumo)
frc = fsc(fft(sumavgo), fft(sumavge))
frc[1][0] = 1.0
for ifreq in range(1, len(frc[0])):
frc[1][ifreq] = max(0.0, frc[1][ifreq])
frc[1][ifreq] = 2. * frc[1][ifreq] / (1. + frc[1][ifreq])
sumavg = Util.addn_img(fft(avgo), fft(avge))
sumctf2 = Util.addn_img(ctf_2_sume, ctf_2_sumo)
Util.div_img(sumavg, sumctf2)
return fft(sumavg), frc, params_list
else:
avge, avgo, params_list = sum_oe(mlist, "a", False, None, False, True)
avge = cosinemask(avge, radius)
avgo = cosinemask(avgo, radius)
frc = fsc(avgo, avge)
frc[1][0] = 1.0
for ifreq in range(1, len(frc[0])):
frc[1][ifreq] = max(0.0, frc[1][ifreq])
frc[1][ifreq] = 2. * frc[1][ifreq] / (1. + frc[1][ifreq])
return avge + avgo, frc, params_list
def apply_enhancement(avg, B_start, pixel_size, user_defined_Bfactor): from filter import filt_gaussinv from fundamentals import rot_avg_table from morphology import compute_bfactor from EMAN2 import periodogram if user_defined_Bfactor>0.0: global_b = user_defined_Bfactor else: guinierline = rot_avg_table(power(periodogram(fft(avg)),.5)) freq_max = 1./(2.*pixel_size) freq_min = 1./B_start b, junk, ifreqmin, ifreqmax = compute_bfactor(guinierline, freq_min, freq_max, pixel_size) #print(ifreqmin, ifreqmax) global_b = b*4. # return filt_gaussinv(fft(avg), sqrt(2./global_b)), global_b
def prepg(image, kb):
"""
Name
prepg - prepare 2-D image for rotation/shift using gridding method.
Input
image: input image that is going to be rotated and shifted using rtshgkb
kb: interpolants (tabulated Kaiser-Bessel function)
Output
imageft: image prepared for gridding rotation and shift
"""
from EMAN2 import Processor
M = image.get_xsize()
# padd two times
npad = 2
N = M*npad
# support of the window
K = 6
alpha = 1.75
r = M/2
v = K/2.0/N
# first pad it with zeros in Fourier space
o = image.FourInterpol(2*M,2*M,1,0)
params = {"filter_type" : Processor.fourier_filter_types.KAISER_SINH_INVERSE,
"alpha" : alpha, "K":K,"r":r,"v":v,"N":N}
q = Processor.EMFourierFilter(o,params)
return fft(q)
def fpol(image, nnx, nny=1, nnz=1, RetReal = True): """ Name fpol -Interpolate image up by padding its Fourier transform with zeroes Input image: image to be interpolated. nnx: new nx dimension nny: new ny dimension (default = 0, meaning equal to the original ny) nnz: new nz dimension (default = 0, meaning equal to the original nz) RetReal: Logical flag, if True, the returned image is real, if False, it is Fourier Output the output interpolated up image """ from fundamentals import fft nx = image.get_xsize() ny = image.get_ysize() nz = image.get_zsize() if image.is_complex(): nx -= (2-nx%2) if nx == nnx and ny == nny and nz == nnz: if image.is_complex() and RetReal: return fft(image) else: return image return image.FourInterpol(nnx, nny, nnz, RetReal)
def prgs1d( prjft, kb, params ):
from fundamentals import fft
from math import cos, sin, pi
from EMAN2 import Processor
alpha = params[0]
shift = params[1]
tmp = alpha/180.0*pi
nuxnew = cos(tmp)
nuynew = -sin(tmp)
line = prjft.extractline(kb, nuxnew, nuynew)
line = fft(line)
M = line.get_xsize()/2
Util.cyclicshift( line, {"dx":M, "dy":0, "dz":0} )
line = Util.window( line, M, 1, 1, 0, 0, 0 )
if shift!=0:
filt_params = {"filter_type" : Processor.fourier_filter_types.SHIFT,
"x_shift" : shift, "y_shift" : 0.0, "z_shift" : 0.0}
line = Processor.EMFourierFilter(temp, filt_params)
line.set_attr_dict( {'alpha':alpha, 's1x':shift} )
return line
def prepi3D(image):
"""
Name
prepi3D - prepare 3-D image for rotation/shift
Input
image: input image that is going to be rotated and shifted using rot_shif3D_grid
Output
imageft: image prepared for gridding rotation and shift
kb: interpolants (tabulated Kaiser-Bessel function)
"""
from EMAN2 import Processor
M = image.get_xsize()
# padding size:
npad = 2
N = M*npad
# support of the window:
K = 6
alpha = 1.75
r = M/2
v = K/2.0/N
kb = Util.KaiserBessel(alpha, K, r, v, N)
# pad with zeros in Fourier space:
q = image.FourInterpol(N, N, N, 0)
params = {"filter_type": Processor.fourier_filter_types.KAISER_SINH_INVERSE,
"alpha":alpha, "K":K, "r":r, "v":v, "N":N}
q = Processor.EMFourierFilter(q, params)
params = {"filter_type" : Processor.fourier_filter_types.TOP_HAT_LOW_PASS,
"cutoff_abs" : 0.25, "dopad" : False}
q = Processor.EMFourierFilter(q, params)
return fft(q), kb
def rotshift2dg(image, ang, dx, dy, kb, scale=1.0):
"""Rotate and shift an image using gridding
"""
from math import radians
from EMAN2 import Processor
M = image.get_xsize()
alpha = 1.75
K = 6
N = M * 2 # npad*image size
r = M / 2
v = K / 2.0 / N
# first pad it with zeros in Fourier space
o = image.FourInterpol(N, N, 1, 0)
params = {
"filter_type": Processor.fourier_filter_types.KAISER_SINH_INVERSE,
"alpha": alpha,
"K": K,
"r": r,
"v": v,
"N": N
}
q = Processor.EMFourierFilter(o, params)
o = fft(q)
# gridding rotation
return o.rot_scale_conv(radians(ang), dx, dy, kb, scale)
def prepg(image, kb):
"""
Name
prepg - prepare 2-D image for rotation/shift using gridding method.
Input
image: input image that is going to be rotated and shifted using rtshgkb
kb: interpolants (tabulated Kaiser-Bessel function)
Output
imageft: image prepared for gridding rotation and shift
"""
from EMAN2 import Processor
M = image.get_xsize()
# padd two times
npad = 2
N = M*npad
# support of the window
K = 6
alpha = 1.75
r = M/2
v = K/2.0/N
# first pad it with zeros in Fourier space
o = image.FourInterpol(2*M,2*M,1,0)
params = {"filter_type" : Processor.fourier_filter_types.KAISER_SINH_INVERSE,
"alpha" : alpha, "K":K,"r":r,"v":v,"N":N}
q = Processor.EMFourierFilter(o,params)
return fft(q)
def prgs1d(prjft, kb, params):
from fundamentals import fft
from math import cos, sin, pi
from EMAN2 import Processor
alpha = params[0]
shift = params[1]
tmp = alpha / 180.0 * pi
nuxnew = cos(tmp)
nuynew = -sin(tmp)
line = prjft.extractline(kb, nuxnew, nuynew)
line = fft(line)
M = line.get_xsize() / 2
Util.cyclicshift(line, {"dx": M, "dy": 0, "dz": 0})
line = Util.window(line, M, 1, 1, 0, 0, 0)
if shift != 0:
filt_params = {
"filter_type": Processor.fourier_filter_types.SHIFT,
"x_shift": shift,
"y_shift": 0.0,
"z_shift": 0.0
}
line = Processor.EMFourierFilter(temp, filt_params)
line.set_attr_dict({'alpha': alpha, 's1x': shift})
return line
def prepi(image):
"""
Name
prepi - prepare 2-D image for rotation/shift
Input
image: input image that is going to be rotated and shifted using rtshgkb
Output
imageft: real space image prepared for gridding rotation and shift by convolution
kb: interpolants (tabulated Kaiser-Bessel function)
"""
from EMAN2 import Processor
M = image.get_xsize()
# pad two times
npad = 2
N = M*npad
# support of the window
K = 6
alpha = 1.75
r = M/2
v = K/2.0/N
kb = Util.KaiserBessel(alpha, K, r, v, N)
#out = rotshift2dg(image, angle*pi/180., sx, sy, kb,alpha)
# first pad it with zeros in Fourier space
q = image.FourInterpol(N, N, 1, 0)
params = {"filter_type": Processor.fourier_filter_types.KAISER_SINH_INVERSE,
"alpha":alpha, "K":K, "r":r, "v":v, "N":N}
q = Processor.EMFourierFilter(q, params)
params = {"filter_type" : Processor.fourier_filter_types.TOP_HAT_LOW_PASS,
"cutoff_abs" : 0.25, "dopad" : False}
q = Processor.EMFourierFilter(q, params)
return fft(q), kb
def prepi3D(image):
"""
Name
prepi3D - prepare 3-D image for rotation/shift
Input
image: input image that is going to be rotated and shifted using rot_shif3D_grid
Output
imageft: image prepared for gridding rotation and shift
kb: interpolants (tabulated Kaiser-Bessel function)
"""
from EMAN2 import Processor
M = image.get_xsize()
# padding size:
npad = 2
N = M*npad
# support of the window:
K = 6
alpha = 1.75
r = M/2
v = K/2.0/N
kb = Util.KaiserBessel(alpha, K, r, v, N)
# pad with zeros in Fourier space:
q = image.FourInterpol(N, N, N, 0)
params = {"filter_type": Processor.fourier_filter_types.KAISER_SINH_INVERSE,
"alpha":alpha, "K":K, "r":r, "v":v, "N":N}
q = Processor.EMFourierFilter(q, params)
params = {"filter_type" : Processor.fourier_filter_types.TOP_HAT_LOW_PASS,
"cutoff_abs" : 0.25, "dopad" : False}
q = Processor.EMFourierFilter(q, params)
return fft(q), kb
def apply_enhancement(avg, B_start, pixel_size, user_defined_Bfactor):
guinierline = rot_avg_table(power(periodogram(avg), .5))
freq_max = 1. / (2. * pixel_size)
freq_min = 1. / B_start
b, junk, ifreqmin, ifreqmax = compute_bfactor(guinierline, freq_min,
freq_max, pixel_size)
print(ifreqmin, ifreqmax)
global_b = b * 4. #
if user_defined_Bfactor < 0.0: global_b = user_defined_Bfactor
sigma_of_inverse = sqrt(2. / global_b)
avg = filt_gaussinv(fft(avg), sigma_of_inverse)
return avg, global_b
def compute_average_noctf(mlist, radius):
from fundamentals import fft
params_list = [None] * len(mlist)
orig_image_size = mlist[0].get_xsize()
avgo = EMData(orig_image_size, orig_image_size, 1, False) #
avge = EMData(orig_image_size, orig_image_size, 1, False) #
for im in xrange(len(mlist)):
alpha, sx, sy, mr, scale = get_params2D(mlist[im],
xform="xform.align2d")
params_list[im] = [alpha, sx, sy, mr, scale]
tmp = cosinemask(rot_shift2D(mlist[im], alpha, sx, sy, mr), radius)
tmp = fft(tmp)
if im % 2 == 0: Util.add_img(avge, tmp)
else: Util.add_img(avgo, tmp)
frc = fsc(fft(avge), fft(avgo))
frc[1][0] = 1.0
for ifreq in xrange(1, len(frc[0])):
frc[1][ifreq] = max(0.0, frc[1][ifreq])
frc[1][ifreq] = 2. * frc[1][ifreq] / (1. + frc[1][ifreq])
sumavg = Util.addn_img(avgo, avge)
sumavg = fft(sumavg)
return sumavg, frc, params_list
def compute_average_ctf(mlist, radius):
from morphology import ctf_img
from filter import filt_ctf, filt_table
from fundamentals import fft, fftip
params_list = [None] * len(mlist)
orig_image_size = mlist[0].get_xsize()
avgo = EMData(orig_image_size, orig_image_size, 1, False) #
avge = EMData(orig_image_size, orig_image_size, 1, False) #
ctf_2_sumo = EMData(orig_image_size, orig_image_size, 1, False)
ctf_2_sume = EMData(orig_image_size, orig_image_size, 1, False)
for im in xrange(len(mlist)):
ctt = ctf_img(orig_image_size, mlist[im].get_attr("ctf"))
alpha, sx, sy, mr, scale = get_params2D(mlist[im],
xform="xform.align2d")
tmp = cosinemask(rot_shift2D(mlist[im], alpha, sx, sy, mr), radius)
params_list[im] = [alpha, sx, sy, mr, scale]
tmp = fft(tmp)
Util.mul_img(tmp, ctt)
#ima_filt = filt_ctf(tmp, ctf_params, dopad=False)
if im % 2 == 0:
Util.add_img2(ctf_2_sume, ctt)
Util.add_img(avge, tmp)
else:
Util.add_img2(ctf_2_sumo, ctt)
Util.add_img(avgo, tmp)
sumavg = Util.divn_img(avge, ctf_2_sume)
sumctf2 = Util.divn_img(avgo, ctf_2_sumo)
frc = fsc(fft(sumavg), fft(sumctf2))
frc[1][0] = 1.0
for ifreq in xrange(1, len(frc[0])):
frc[1][ifreq] = max(0.0, frc[1][ifreq])
frc[1][ifreq] = 2. * frc[1][ifreq] / (1. + frc[1][ifreq])
sumavg = Util.addn_img(avgo, avge)
sumctf2 = Util.addn_img(ctf_2_sume, ctf_2_sumo)
Util.div_img(sumavg, sumctf2)
sumavg = fft(sumavg)
return sumavg, frc, params_list
def gridrot_shift2D(image, ang=0.0, sx=0.0, sy=0.0, scale=1.0):
"""
Rotate and shift an image using gridding in Fourier space.
"""
from EMAN2 import Processor
from fundamentals import fftip, fft
nx = image.get_xsize()
# split shift into integer and fractional parts
isx = int(sx)
fsx = sx - isx
isy = int(sy)
fsy = sy - isy
# prepare
npad = 2
N = nx * npad
K = 6
alpha = 1.75
r = nx / 2
v = K / 2.0 / N
kb = Util.KaiserBessel(alpha, K, r, v, N)
image1 = image.copy(
) # This step is needed, otherwise image will be changed outside the function
# divide out gridding weights
image1.divkbsinh(kb)
# pad and center image, then FFT
image1 = image1.norm_pad(False, npad)
fftip(image1)
# Put the origin of the (real-space) image at the center
image1.center_origin_fft()
# gridding rotation
image1 = image1.fouriergridrot2d(ang, scale, kb)
if (fsx != 0.0 or fsy != 0.0):
params = {
"filter_type": Processor.fourier_filter_types.SHIFT,
"x_shift": float(fsx),
"y_shift": float(fsy),
"z_shift": 0.0
}
image1 = Processor.EMFourierFilter(image1, params)
# put the origin back in the corner
image1.center_origin_fft()
# undo FFT and remove padding (window)
image1 = fft(image1)
image1 = image1.window_center(nx)
Util.cyclicshift(image1, {"dx": isx, "dy": isy, "dz": 0})
return image1
def cml_sinogram_shift(image2D, diameter, shifts=[0.0, 0.0], d_psi=1):
from math import cos, sin
from fundamentals import fft
M_PI = 3.141592653589793238462643383279502884197
# prepare
M = image2D.get_xsize()
# padd two times
npad = 2
N = M * npad
# support of the window
K = 6
alpha = 1.75
r = M / 2
v = K / 2.0 / N
kb = Util.KaiserBessel(alpha, K, r, K / (2. * N), N)
volft = image2D.average_circ_sub() # ASTA - in spider
volft.divkbsinh(kb) # DIVKB2 - in spider
volft = volft.norm_pad(False, npad)
volft.do_fft_inplace()
# Apply shift
from EMAN2 import Processor
params2 = {
"filter_type": Processor.fourier_filter_types.SHIFT,
"x_shift": 2 * shifts[0],
"y_shift": 2 * shifts[1],
"z_shift": 0.0
}
volft = Processor.EMFourierFilter(volft, params2)
volft.center_origin_fft()
volft.fft_shuffle()
# get line projection
nangle = int(180.0 / d_psi)
dangle = M_PI / float(nangle)
e = EMData()
e.set_size(diameter, nangle, 1)
offset = M - diameter // 2
for j in xrange(nangle):
nuxnew = cos(dangle * j)
nuynew = -sin(dangle * j)
line = fft(volft.extractline(kb, nuxnew, nuynew))
Util.cyclicshift(line, {"dx": M, "dy": 0, "dz": 0})
Util.set_line(e, j, line, offset, diameter)
return e
def cml_sinogram_shift(image2D, diameter, shifts = [0.0, 0.0], d_psi = 1):
from math import cos, sin
from fundamentals import fft
M_PI = 3.141592653589793238462643383279502884197
# prepare
M = image2D.get_xsize()
# padd two times
npad = 2
N = M * npad
# support of the window
K = 6
alpha = 1.75
r = M / 2
v = K / 2.0 / N
kb = Util.KaiserBessel(alpha, K, r, K / (2. * N), N)
volft = image2D.average_circ_sub() # ASTA - in spider
volft.divkbsinh(kb) # DIVKB2 - in spider
volft = volft.norm_pad(False, npad)
volft.do_fft_inplace()
# Apply shift
from EMAN2 import Processor
params2 = {"filter_type" : Processor.fourier_filter_types.SHIFT, "x_shift" : 2*shifts[0], "y_shift" : 2*shifts[1], "z_shift" : 0.0}
volft = Processor.EMFourierFilter(volft, params2)
volft.center_origin_fft()
volft.fft_shuffle()
# get line projection
nangle = int(180.0 / d_psi)
dangle = M_PI / float(nangle)
e = EMData()
e.set_size(diameter, nangle, 1)
offset = M - diameter // 2
for j in xrange(nangle):
nuxnew = cos(dangle * j)
nuynew = -sin(dangle * j)
line = fft(volft.extractline(kb, nuxnew, nuynew))
Util.cyclicshift(line, {"dx":M, "dy":0, "dz":0})
Util.set_line(e, j, line, offset, diameter)
return e
def gridrot_shift2D(image, ang = 0.0, sx = 0.0, sy = 0.0, scale = 1.0):
"""
Rotate and shift an image using gridding on Fourier space.
"""
from EMAN2 import Processor
from fundamentals import fftip, fft
nx = image.get_xsize()
# split shift into integer and fractional parts
isx = int(sx)
fsx = sx - isx
isy = int(sy)
fsy = sy - isy
# prepare
npad = 2
N = nx*npad
K = 6
alpha = 1.75
r = nx/2
v = K/2.0/N
kb = Util.KaiserBessel(alpha, K, r, v, N)
image1 = image.copy() # This step is needed, otherwise image will be changed outside the function
# divide out gridding weights
image1.divkbsinh(kb)
# pad and center image, then FFT
image1 = image1.norm_pad(False, npad)
fftip(image1)
# Put the origin of the (real-space) image at the center
image1.center_origin_fft()
# gridding rotation
image1 = image1.fouriergridrot2d(ang, scale, kb)
if(fsx != 0.0 or fsy != 0.0):
params = {"filter_type" : Processor.fourier_filter_types.SHIFT, "x_shift" : float(fsx), "y_shift" : float(fsy), "z_shift" : 0.0 }
image1 = Processor.EMFourierFilter(image1, params)
# put the origin back in the corner
image1.center_origin_fft()
# undo FFT and remove padding (window)
image1 = fft(image1)
image1 = image1.window_center(nx)
Util.cyclicshift(image1,{"dx":isx,"dy":isy,"dz":0})
return image1
def rotshift2dg(image, ang, dx, dy, kb, scale = 1.0):
"""Rotate and shift an image using gridding
"""
from math import pi
from EMAN2 import Processor
M = image.get_xsize()
alpha = 1.75
K = 6
N = M*2 # npad*image size
r = M/2
v = K/2.0/N
# first pad it with zeros in Fourier space
o = image.FourInterpol(2*M,2*M,1,0)
params = {"filter_type" : Processor.fourier_filter_types.KAISER_SINH_INVERSE,
"alpha" : alpha, "K":K,"r":r,"v":v,"N":N}
q = Processor.EMFourierFilter(o,params)
o = fft(q)
# gridding rotation
return o.rot_scale_conv(ang*pi/180.0, dx, dy, kb, scale)
def cml_sinogram(image2D, diameter, d_psi = 1):
from math import cos, sin
from fundamentals import fft
M_PI = 3.141592653589793238462643383279502884197
# prepare
M = image2D.get_xsize()
# padd two times
npad = 2
N = M * npad
# support of the window
K = 6
alpha = 1.75
r = old_div(M, 2)
v = K / 2.0 / N
kb = Util.KaiserBessel(alpha, K, r, old_div(K, (2. * N)), N)
volft = image2D.average_circ_sub() # ASTA - in spider
volft.divkbsinh(kb) # DIVKB2 - in spider
volft = volft.norm_pad(False, npad)
volft.do_fft_inplace()
volft.center_origin_fft()
volft.fft_shuffle()
# get line projection
nangle = int(180.0 / d_psi)
dangle = M_PI / float(nangle)
e = EMData()
e.set_size(diameter, nangle, 1)
offset = M - diameter // 2
for j in range(nangle):
nuxnew = cos(dangle * j)
nuynew = -sin(dangle * j)
line = fft(volft.extractline(kb, nuxnew, nuynew))
Util.cyclicshift(line, {"dx":M, "dy":0, "dz":0})
Util.set_line(e, j, line, offset, diameter)
return e
def do_volume_mrk02(ref_data):
"""
data - projections (scattered between cpus) or the volume. If volume, just do the volume processing
options - the same for all cpus
return - volume the same for all cpus
"""
from EMAN2 import Util
from mpi import mpi_comm_rank, mpi_comm_size, MPI_COMM_WORLD
from filter import filt_table
from reconstruction import recons3d_4nn_MPI, recons3d_4nn_ctf_MPI
from utilities import bcast_EMData_to_all, bcast_number_to_all, model_blank
from fundamentals import rops_table, fftip, fft
import types
# Retrieve the function specific input arguments from ref_data
data = ref_data[0]
Tracker = ref_data[1]
iter = ref_data[2]
mpi_comm = ref_data[3]
# # For DEBUG
# print "Type of data %s" % (type(data))
# print "Type of Tracker %s" % (type(Tracker))
# print "Type of iter %s" % (type(iter))
# print "Type of mpi_comm %s" % (type(mpi_comm))
if(mpi_comm == None): mpi_comm = MPI_COMM_WORLD
myid = mpi_comm_rank(mpi_comm)
nproc = mpi_comm_size(mpi_comm)
try: local_filter = Tracker["local_filter"]
except: local_filter = False
#=========================================================================
# volume reconstruction
if( type(data) == types.ListType ):
if Tracker["constants"]["CTF"]:
vol = recons3d_4nn_ctf_MPI(myid, data, Tracker["constants"]["snr"], \
symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm, smearstep = Tracker["smearstep"])
else:
vol = recons3d_4nn_MPI (myid, data,\
symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm)
else:
vol = data
if myid == 0:
from morphology import threshold
from filter import filt_tanl, filt_btwl
from utilities import model_circle, get_im
import types
nx = vol.get_xsize()
if(Tracker["constants"]["mask3D"] == None):
mask3D = model_circle(int(Tracker["constants"]["radius"]*float(nx)/float(Tracker["constants"]["nnxo"])+0.5), nx, nx, nx)
elif(Tracker["constants"]["mask3D"] == "auto"):
from utilities import adaptive_mask
mask3D = adaptive_mask(vol)
else:
if( type(Tracker["constants"]["mask3D"]) == types.StringType ): mask3D = get_im(Tracker["constants"]["mask3D"])
else: mask3D = (Tracker["constants"]["mask3D"]).copy()
nxm = mask3D.get_xsize()
if( nx != nxm):
from fundamentals import rot_shift3D
mask3D = Util.window(rot_shift3D(mask3D,scale=float(nx)/float(nxm)),nx,nx,nx)
nxm = mask3D.get_xsize()
assert(nx == nxm)
stat = Util.infomask(vol, mask3D, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0/stat[1])
vol = threshold(vol)
Util.mul_img(vol, mask3D)
if( Tracker["PWadjustment"] ):
from utilities import read_text_file, write_text_file
rt = read_text_file( Tracker["PWadjustment"] )
fftip(vol)
ro = rops_table(vol)
# Here unless I am mistaken it is enough to take the beginning of the reference pw.
for i in xrange(1,len(ro)): ro[i] = (rt[i]/ro[i])**Tracker["upscale"]
#write_text_file(rops_table(filt_table( vol, ro),1),"foo.txt")
if Tracker["constants"]["sausage"]:
ny = vol.get_ysize()
y = float(ny)
from math import exp
for i in xrange(len(ro)): ro[i] *= \
(1.0+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.10)/0.025)**2)+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.215)/0.025)**2))
if local_filter:
# skip low-pass filtration
vol = fft( filt_table( vol, ro) )
else:
if( type(Tracker["lowpass"]) == types.ListType ):
vol = fft( filt_table( filt_table(vol, Tracker["lowpass"]), ro) )
else:
vol = fft( filt_table( filt_tanl(vol, Tracker["lowpass"], Tracker["falloff"]), ro) )
del ro
else:
if Tracker["constants"]["sausage"]:
ny = vol.get_ysize()
y = float(ny)
ro = [0.0]*(ny//2+2)
from math import exp
for i in xrange(len(ro)): ro[i] = \
(1.0+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.10)/0.025)**2)+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.215)/0.025)**2))
fftip(vol)
filt_table(vol, ro)
del ro
if not local_filter:
if( type(Tracker["lowpass"]) == types.ListType ):
vol = filt_table(vol, Tracker["lowpass"])
else:
vol = filt_tanl(vol, Tracker["lowpass"], Tracker["falloff"])
if Tracker["constants"]["sausage"]: vol = fft(vol)
if local_filter:
from morphology import binarize
if(myid == 0): nx = mask3D.get_xsize()
else: nx = 0
nx = bcast_number_to_all(nx, source_node = 0)
# only main processor needs the two input volumes
if(myid == 0):
mask = binarize(mask3D, 0.5)
locres = get_im(Tracker["local_filter"])
lx = locres.get_xsize()
if(lx != nx):
if(lx < nx):
from fundamentals import fdecimate, rot_shift3D
mask = Util.window(rot_shift3D(mask,scale=float(lx)/float(nx)),lx,lx,lx)
vol = fdecimate(vol, lx,lx,lx)
else: ERROR("local filter cannot be larger than input volume","user function",1)
stat = Util.infomask(vol, mask, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0/stat[1])
else:
lx = 0
locres = model_blank(1,1,1)
vol = model_blank(1,1,1)
lx = bcast_number_to_all(lx, source_node = 0)
if( myid != 0 ): mask = model_blank(lx,lx,lx)
bcast_EMData_to_all(mask, myid, 0, comm=mpi_comm)
from filter import filterlocal
vol = filterlocal( locres, vol, mask, Tracker["falloff"], myid, 0, nproc)
if myid == 0:
if(lx < nx):
from fundamentals import fpol
vol = fpol(vol, nx,nx,nx)
vol = threshold(vol)
vol = filt_btwl(vol, 0.38, 0.5)# This will have to be corrected.
Util.mul_img(vol, mask3D)
del mask3D
# vol.write_image('toto%03d.hdf'%iter)
else:
vol = model_blank(nx,nx,nx)
else:
if myid == 0:
#from utilities import write_text_file
#write_text_file(rops_table(vol,1),"goo.txt")
stat = Util.infomask(vol, mask3D, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0/stat[1])
vol = threshold(vol)
vol = filt_btwl(vol, 0.38, 0.5)# This will have to be corrected.
Util.mul_img(vol, mask3D)
del mask3D
# vol.write_image('toto%03d.hdf'%iter)
# broadcast volume
bcast_EMData_to_all(vol, myid, 0, comm=mpi_comm)
#=========================================================================
return vol
def dovolume( ref_data ):
from utilities import print_msg, read_text_row
from filter import fit_tanh, filt_tanl
from fundamentals import fshift
from morphology import threshold
# Prepare the reference in 3D alignment, this function corresponds to what do_volume does.
# Input: list ref_data
# 0 - mask
# 1 - center flag
# 2 - raw average
# 3 - fsc result
# Output: filtered, centered, and masked reference image
# apply filtration (FSC) to reference image:
global ref_ali2d_counter
ref_ali2d_counter += 1
fl = ref_data[2].cmp("dot",ref_data[2], {"negative":0, "mask":ref_data[0]} )
print_msg("do_volume user function Step = %5d GOAL = %10.3e\n"%(ref_ali2d_counter,fl))
stat = Util.infomask(ref_data[2], ref_data[0], False)
vol = ref_data[2] - stat[0]
Util.mul_scalar(vol, 1.0/stat[1])
vol = threshold(vol)
#Util.mul_img(vol, ref_data[0])
try:
aa = read_text_row("flaa.txt")[0]
fl = aa[0]
aa=aa[1]
except:
fl = 0.4
aa = 0.2
msg = "Tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n"%(fl, aa)
print_msg(msg)
from utilities import read_text_file
from fundamentals import rops_table, fftip, fft
from filter import filt_table, filt_btwl
fftip(vol)
try:
rt = read_text_file( "pwreference.txt" )
ro = rops_table(vol)
# Here unless I am mistaken it is enough to take the beginning of the reference pw.
for i in xrange(1,len(ro)): ro[i] = (rt[i]/ro[i])**0.5
vol = fft( filt_table( filt_tanl(vol, fl, aa), ro) )
msg = "Power spectrum adjusted\n"
print_msg(msg)
except:
vol = fft( filt_tanl(vol, fl, aa) )
stat = Util.infomask(vol, ref_data[0], False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0/stat[1])
vol = threshold(vol)
vol = filt_btwl(vol, 0.38, 0.5)
Util.mul_img(vol, ref_data[0])
if ref_data[1] == 1:
cs = volf.phase_cog()
msg = "Center x = %10.3f Center y = %10.3f Center z = %10.3f\n"%(cs[0], cs[1], cs[2])
print_msg(msg)
volf = fshift(volf, -cs[0], -cs[1], -cs[2])
else: cs = [0.0]*3
return vol, cs
def main():
arglist = []
for arg in sys.argv:
arglist.append(arg)
progname = os.path.basename(arglist[0])
usage = progname + """ firstvolume secondvolume maskfile outputfile --wn --step --cutoff --radius --fsc --res_overall --out_ang_res --apix --MPI
Compute local resolution in real space within area outlined by the maskfile and within regions wn x wn x wn
"""
parser = optparse.OptionParser(usage, version=global_def.SPARXVERSION)
parser.add_option(
"--wn",
type="int",
default=7,
help=
"Size of window within which local real-space FSC is computed. (default 7)"
)
parser.add_option(
"--step",
type="float",
default=1.0,
help="Shell step in Fourier size in pixels. (default 1.0)")
parser.add_option("--cutoff",
type="float",
default=0.5,
help="Resolution cut-off for FSC. (default 0.5)")
parser.add_option(
"--radius",
type="int",
default=-1,
help=
"If there is no maskfile, sphere with r=radius will be used. By default, the radius is nx/2-wn (default -1)"
)
parser.add_option(
"--fsc",
type="string",
default=None,
help=
"Save overall FSC curve (might be truncated). By default, the program does not save the FSC curve. (default none)"
)
parser.add_option(
"--res_overall",
type="float",
default=-1.0,
help=
"Overall resolution at the cutoff level estimated by the user [abs units]. (default None)"
)
parser.add_option(
"--out_ang_res",
action="store_true",
default=False,
help=
"Additionally creates a local resolution file in Angstroms. (default False)"
)
parser.add_option(
"--apix",
type="float",
default=1.0,
help=
"Pixel size in Angstrom. Effective only with --out_ang_res options. (default 1.0)"
)
parser.add_option("--MPI",
action="store_true",
default=False,
help="Use MPI version.")
(options, args) = parser.parse_args(arglist[1:])
if len(args) < 3 or len(args) > 4:
print("See usage " + usage)
sys.exit()
if global_def.CACHE_DISABLE:
utilities.disable_bdb_cache()
res_overall = options.res_overall
if options.MPI:
sys.argv = mpi.mpi_init(len(sys.argv), sys.argv)
number_of_proc = mpi.mpi_comm_size(mpi.MPI_COMM_WORLD)
myid = mpi.mpi_comm_rank(mpi.MPI_COMM_WORLD)
main_node = 0
global_def.MPI = True
cutoff = options.cutoff
nk = int(options.wn)
if (myid == main_node):
#print sys.argv
vi = utilities.get_im(sys.argv[1])
ui = utilities.get_im(sys.argv[2])
nx = vi.get_xsize()
ny = vi.get_ysize()
nz = vi.get_zsize()
dis = [nx, ny, nz]
else:
dis = [0, 0, 0, 0]
global_def.BATCH = True
dis = utilities.bcast_list_to_all(dis, myid, source_node=main_node)
if (myid != main_node):
nx = int(dis[0])
ny = int(dis[1])
nz = int(dis[2])
vi = utilities.model_blank(nx, ny, nz)
ui = utilities.model_blank(nx, ny, nz)
if len(args) == 3:
m = utilities.model_circle((min(nx, ny, nz) - nk) // 2, nx, ny, nz)
outvol = args[2]
elif len(args) == 4:
if (myid == main_node):
m = morphology.binarize(utilities.get_im(args[2]), 0.5)
else:
m = utilities.model_blank(nx, ny, nz)
outvol = args[3]
utilities.bcast_EMData_to_all(m, myid, main_node)
"""Multiline Comment0"""
freqvol, resolut = statistics.locres(vi, ui, m, nk, cutoff,
options.step, myid, main_node,
number_of_proc)
if (myid == 0):
# Remove outliers based on the Interquartile range
output_volume(freqvol, resolut, options.apix, outvol, options.fsc,
options.out_ang_res, nx, ny, nz, res_overall)
mpi.mpi_finalize()
else:
cutoff = options.cutoff
vi = utilities.get_im(args[0])
ui = utilities.get_im(args[1])
nn = vi.get_xsize()
nk = int(options.wn)
if len(args) == 3:
m = utilities.model_circle((nn - nk) // 2, nn, nn, nn)
outvol = args[2]
elif len(args) == 4:
m = morphology.binarize(utilities.get_im(args[2]), 0.5)
outvol = args[3]
mc = utilities.model_blank(nn, nn, nn, 1.0) - m
vf = fundamentals.fft(vi)
uf = fundamentals.fft(ui)
"""Multiline Comment1"""
lp = int(nn / 2 / options.step + 0.5)
step = 0.5 / lp
freqvol = utilities.model_blank(nn, nn, nn)
resolut = []
for i in range(1, lp):
fl = step * i
fh = fl + step
#print(lp,i,step,fl,fh)
v = fundamentals.fft(filter.filt_tophatb(vf, fl, fh))
u = fundamentals.fft(filter.filt_tophatb(uf, fl, fh))
tmp1 = EMAN2_cppwrap.Util.muln_img(v, v)
tmp2 = EMAN2_cppwrap.Util.muln_img(u, u)
do = EMAN2_cppwrap.Util.infomask(
morphology.square_root(
morphology.threshold(
EMAN2_cppwrap.Util.muln_img(tmp1, tmp2))), m, True)[0]
tmp3 = EMAN2_cppwrap.Util.muln_img(u, v)
dp = EMAN2_cppwrap.Util.infomask(tmp3, m, True)[0]
resolut.append([i, (fl + fh) / 2.0, dp / do])
tmp1 = EMAN2_cppwrap.Util.box_convolution(tmp1, nk)
tmp2 = EMAN2_cppwrap.Util.box_convolution(tmp2, nk)
tmp3 = EMAN2_cppwrap.Util.box_convolution(tmp3, nk)
EMAN2_cppwrap.Util.mul_img(tmp1, tmp2)
tmp1 = morphology.square_root(morphology.threshold(tmp1))
EMAN2_cppwrap.Util.mul_img(tmp1, m)
EMAN2_cppwrap.Util.add_img(tmp1, mc)
EMAN2_cppwrap.Util.mul_img(tmp3, m)
EMAN2_cppwrap.Util.add_img(tmp3, mc)
EMAN2_cppwrap.Util.div_img(tmp3, tmp1)
EMAN2_cppwrap.Util.mul_img(tmp3, m)
freq = (fl + fh) / 2.0
bailout = True
for x in range(nn):
for y in range(nn):
for z in range(nn):
if (m.get_value_at(x, y, z) > 0.5):
if (freqvol.get_value_at(x, y, z) == 0.0):
if (tmp3.get_value_at(x, y, z) < cutoff):
freqvol.set_value_at(x, y, z, freq)
bailout = False
else:
bailout = False
if (bailout): break
#print(len(resolut))
# remove outliers
output_volume(freqvol, resolut, options.apix, outvol, options.fsc,
options.out_ang_res, nx, ny, nz, res_overall)
def main():
import sys
import os
import math
import random
import pyemtbx.options
import time
from random import random, seed, randint
from optparse import OptionParser
progname = os.path.basename(sys.argv[0])
usage = progname + """ [options] <inputfile> <outputfile>
Generic 2-D image processing programs.
Functionality:
1. Phase flip a stack of images and write output to new file:
sxprocess.py input_stack.hdf output_stack.hdf --phase_flip
2. Resample (decimate or interpolate up) images (2D or 3D) in a stack to change the pixel size.
The window size will change accordingly.
sxprocess input.hdf output.hdf --changesize --ratio=0.5
3. Compute average power spectrum of a stack of 2D images with optional padding (option wn) with zeroes.
sxprocess.py input_stack.hdf powerspectrum.hdf --pw [--wn=1024]
4. Generate a stack of projections bdb:data and micrographs with prefix mic (i.e., mic0.hdf, mic1.hdf etc) from structure input_structure.hdf, with CTF applied to both projections and micrographs:
sxprocess.py input_structure.hdf data mic --generate_projections format="bdb":apix=5.2:CTF=True:boxsize=64
5. Retrieve original image numbers in the selected ISAC group (here group 12 from generation 3):
sxprocess.py bdb:test3 class_averages_generation_3.hdf list3_12.txt --isacgroup=12 --params=originalid
6. Retrieve original image numbers of images listed in ISAC output stack of averages:
sxprocess.py select1.hdf ohk.txt
7. Adjust rotationally averaged power spectrum of an image to that of a reference image or a reference 1D power spectrum stored in an ASCII file.
Optionally use a tangent low-pass filter. Also works for a stack of images, in which case the output is also a stack.
sxprocess.py vol.hdf ref.hdf avol.hdf < 0.25 0.2> --adjpw
sxprocess.py vol.hdf pw.txt avol.hdf < 0.25 0.2> --adjpw
8. Generate a 1D rotationally averaged power spectrum of an image.
sxprocess.py vol.hdf --rotwp=rotpw.txt
# Output will contain three columns:
(1) rotationally averaged power spectrum
(2) logarithm of the rotationally averaged power spectrum
(3) integer line number (from zero to approximately to half the image size)
9. Apply 3D transformation (rotation and/or shift) to a set of orientation parameters associated with projection data.
sxprocess.py --transfromparams=phi,theta,psi,tx,ty,tz input.txt output.txt
The output file is then imported and 3D transformed volume computed:
sxheader.py bdb:p --params=xform.projection --import=output.txt
mpirun -np 2 sxrecons3d_n.py bdb:p tvol.hdf --MPI
The reconstructed volume is in the position of the volume computed using the input.txt parameters and then
transformed with rot_shift3D(vol, phi,theta,psi,tx,ty,tz)
10. Import ctf parameters from the output of sxcter into windowed particle headers.
There are three possible input files formats: (1) all particles are in one stack, (2 aor 3) particles are in stacks, each stack corresponds to a single micrograph.
In each case the particles should contain a name of the micrograph of origin stores using attribute name 'ptcl_source_image'.
Normally this is done by e2boxer.py during windowing.
Particles whose defocus or astigmatism error exceed set thresholds will be skipped, otherwise, virtual stacks with the original way preceded by G will be created.
sxprocess.py --input=bdb:data --importctf=outdir/partres --defocuserror=10.0 --astigmatismerror=5.0
# Output will be a vritual stack bdb:Gdata
sxprocess.py --input="bdb:directory/stacks*" --importctf=outdir/partres --defocuserror=10.0 --astigmatismerror=5.0
To concatenate output files:
cd directory
e2bdb.py . --makevstack=bdb:allparticles --filt=G
IMPORTANT: Please do not move (or remove!) any input/intermediate EMAN2DB files as the information is linked between them.
11. Scale 3D shifts. The shifts in the input five columns text file with 3D orientation parameters will be DIVIDED by the scale factor
sxprocess.py orientationparams.txt scaledparams.txt scale=0.5
12. Generate adaptive mask from a given 3-D volume.
"""
parser = OptionParser(usage, version=SPARXVERSION)
parser.add_option(
"--order",
action="store_true",
help=
"Two arguments are required: name of input stack and desired name of output stack. The output stack is the input stack sorted by similarity in terms of cross-correlation coefficent.",
default=False)
parser.add_option("--order_lookup",
action="store_true",
help="Test/Debug.",
default=False)
parser.add_option("--order_metropolis",
action="store_true",
help="Test/Debug.",
default=False)
parser.add_option("--order_pca",
action="store_true",
help="Test/Debug.",
default=False)
parser.add_option(
"--initial",
type="int",
default=-1,
help=
"Specifies which image will be used as an initial seed to form the chain. (default = 0, means the first image)"
)
parser.add_option(
"--circular",
action="store_true",
help=
"Select circular ordering (fisr image has to be similar to the last",
default=False)
parser.add_option(
"--radius",
type="int",
default=-1,
help="Radius of a circular mask for similarity based ordering")
parser.add_option(
"--changesize",
action="store_true",
help=
"resample (decimate or interpolate up) images (2D or 3D) in a stack to change the pixel size.",
default=False)
parser.add_option(
"--ratio",
type="float",
default=1.0,
help=
"The ratio of new to old image size (if <1 the pixel size will increase and image size decrease, if>1, the other way round"
)
parser.add_option(
"--pw",
action="store_true",
help=
"compute average power spectrum of a stack of 2-D images with optional padding (option wn) with zeroes",
default=False)
parser.add_option(
"--wn",
type="int",
default=-1,
help=
"Size of window to use (should be larger/equal than particle box size, default padding to max(nx,ny))"
)
parser.add_option("--phase_flip",
action="store_true",
help="Phase flip the input stack",
default=False)
parser.add_option(
"--makedb",
metavar="param1=value1:param2=value2",
type="string",
action="append",
help=
"One argument is required: name of key with which the database will be created. Fill in database with parameters specified as follows: --makedb param1=value1:param2=value2, e.g. 'gauss_width'=1.0:'pixel_input'=5.2:'pixel_output'=5.2:'thr_low'=1.0"
)
parser.add_option(
"--generate_projections",
metavar="param1=value1:param2=value2",
type="string",
action="append",
help=
"Three arguments are required: name of input structure from which to generate projections, desired name of output projection stack, and desired prefix for micrographs (e.g. if prefix is 'mic', then micrographs mic0.hdf, mic1.hdf etc will be generated). Optional arguments specifying format, apix, box size and whether to add CTF effects can be entered as follows after --generate_projections: format='bdb':apix=5.2:CTF=True:boxsize=100, or format='hdf', etc., where format is bdb or hdf, apix (pixel size) is a float, CTF is True or False, and boxsize denotes the dimension of the box (assumed to be a square). If an optional parameter is not specified, it will default as follows: format='bdb', apix=2.5, CTF=False, boxsize=64."
)
parser.add_option(
"--isacgroup",
type="int",
help=
"Retrieve original image numbers in the selected ISAC group. See ISAC documentation for details.",
default=-1)
parser.add_option(
"--isacselect",
action="store_true",
help=
"Retrieve original image numbers of images listed in ISAC output stack of averages. See ISAC documentation for details.",
default=False)
parser.add_option(
"--params",
type="string",
default=None,
help="Name of header of parameter, which one depends on specific option"
)
parser.add_option(
"--adjpw",
action="store_true",
help="Adjust rotationally averaged power spectrum of an image",
default=False)
parser.add_option(
"--rotpw",
type="string",
default=None,
help=
"Name of the text file to contain rotationally averaged power spectrum of the input image."
)
parser.add_option(
"--transformparams",
type="string",
default=None,
help=
"Transform 3D projection orientation parameters using six 3D parameters (phi, theta,psi,sx,sy,sz). Input: --transformparams=45.,66.,12.,-2,3,-5.5 desired six transformation of the reconstructed structure. Output: file with modified orientation parameters."
)
# import ctf estimates done using cter
parser.add_option("--input",
type="string",
default=None,
help="Input particles.")
parser.add_option(
"--importctf",
type="string",
default=None,
help="Name of the file containing CTF parameters produced by sxcter.")
parser.add_option(
"--defocuserror",
type="float",
default=1000000.0,
help=
"Exclude micrographs whose relative defocus error as estimated by sxcter is larger than defocuserror percent. The error is computed as (std dev defocus)/defocus*100%"
)
parser.add_option(
"--astigmatismerror",
type="float",
default=360.0,
help=
"Set to zero astigmatism for micrographs whose astigmatism angular error as estimated by sxcter is larger than astigmatismerror degrees."
)
# import ctf estimates done using cter
parser.add_option(
"--scale",
type="float",
default=-1.0,
help=
"Divide shifts in the input 3D orientation parameters text file by the scale factor."
)
# generate adaptive mask from an given 3-Db volue
parser.add_option("--adaptive_mask",
action="store_true",
help="create adavptive 3-D mask from a given volume",
default=False)
parser.add_option(
"--nsigma",
type="float",
default=1.,
help=
"number of times of sigma of the input volume to obtain the the large density cluster"
)
parser.add_option(
"--ndilation",
type="int",
default=3,
help=
"number of times of dilation applied to the largest cluster of density"
)
parser.add_option(
"--kernel_size",
type="int",
default=11,
help="convolution kernel for smoothing the edge of the mask")
parser.add_option(
"--gauss_standard_dev",
type="int",
default=9,
help="stanadard deviation value to generate Gaussian edge")
(options, args) = parser.parse_args()
global_def.BATCH = True
if options.phase_flip:
nargs = len(args)
if nargs != 2:
print "must provide name of input and output file!"
return
from EMAN2 import Processor
instack = args[0]
outstack = args[1]
nima = EMUtil.get_image_count(instack)
from filter import filt_ctf
for i in xrange(nima):
img = EMData()
img.read_image(instack, i)
try:
ctf = img.get_attr('ctf')
except:
print "no ctf information in input stack! Exiting..."
return
dopad = True
sign = 1
binary = 1 # phase flip
assert img.get_ysize() > 1
dict = ctf.to_dict()
dz = dict["defocus"]
cs = dict["cs"]
voltage = dict["voltage"]
pixel_size = dict["apix"]
b_factor = dict["bfactor"]
ampcont = dict["ampcont"]
dza = dict["dfdiff"]
azz = dict["dfang"]
if dopad and not img.is_complex(): ip = 1
else: ip = 0
params = {
"filter_type": Processor.fourier_filter_types.CTF_,
"defocus": dz,
"Cs": cs,
"voltage": voltage,
"Pixel_size": pixel_size,
"B_factor": b_factor,
"amp_contrast": ampcont,
"dopad": ip,
"binary": binary,
"sign": sign,
"dza": dza,
"azz": azz
}
tmp = Processor.EMFourierFilter(img, params)
tmp.set_attr_dict({"ctf": ctf})
tmp.write_image(outstack, i)
elif options.changesize:
nargs = len(args)
if nargs != 2:
ERROR("must provide name of input and output file!", "change size",
1)
return
from utilities import get_im
instack = args[0]
outstack = args[1]
sub_rate = float(options.ratio)
nima = EMUtil.get_image_count(instack)
from fundamentals import resample
for i in xrange(nima):
resample(get_im(instack, i), sub_rate).write_image(outstack, i)
elif options.isacgroup > -1:
nargs = len(args)
if nargs != 3:
ERROR("Three files needed on input!", "isacgroup", 1)
return
from utilities import get_im
instack = args[0]
m = get_im(args[1], int(options.isacgroup)).get_attr("members")
l = []
for k in m:
l.append(int(get_im(args[0], k).get_attr(options.params)))
from utilities import write_text_file
write_text_file(l, args[2])
elif options.isacselect:
nargs = len(args)
if nargs != 2:
ERROR("Two files needed on input!", "isacgroup", 1)
return
from utilities import get_im
nima = EMUtil.get_image_count(args[0])
m = []
for k in xrange(nima):
m += get_im(args[0], k).get_attr("members")
m.sort()
from utilities import write_text_file
write_text_file(m, args[1])
elif options.pw:
nargs = len(args)
if nargs < 2:
ERROR("must provide name of input and output file!", "pw", 1)
return
from utilities import get_im
d = get_im(args[0])
nx = d.get_xsize()
ny = d.get_ysize()
if nargs == 3: mask = get_im(args[2])
wn = int(options.wn)
if wn == -1:
wn = max(nx, ny)
else:
if ((wn < nx) or (wn < ny)):
ERROR("window size cannot be smaller than the image size",
"pw", 1)
n = EMUtil.get_image_count(args[0])
from utilities import model_blank, model_circle, pad
from EMAN2 import periodogram
p = model_blank(wn, wn)
for i in xrange(n):
d = get_im(args[0], i)
if nargs == 3:
d *= mask
st = Util.infomask(d, None, True)
d -= st[0]
p += periodogram(pad(d, wn, wn, 1, 0.))
p /= n
p.write_image(args[1])
elif options.adjpw:
if len(args) < 3:
ERROR(
"filt_by_rops input target output fl aa (the last two are optional parameters of a low-pass filter)",
"adjpw", 1)
return
img_stack = args[0]
from math import sqrt
from fundamentals import rops_table, fft
from utilities import read_text_file, get_im
from filter import filt_tanl, filt_table
if (args[1][-3:] == 'txt'):
rops_dst = read_text_file(args[1])
else:
rops_dst = rops_table(get_im(args[1]))
out_stack = args[2]
if (len(args) > 4):
fl = float(args[3])
aa = float(args[4])
else:
fl = -1.0
aa = 0.0
nimage = EMUtil.get_image_count(img_stack)
for i in xrange(nimage):
img = fft(get_im(img_stack, i))
rops_src = rops_table(img)
assert len(rops_dst) == len(rops_src)
table = [0.0] * len(rops_dst)
for j in xrange(len(rops_dst)):
table[j] = sqrt(rops_dst[j] / rops_src[j])
if (fl > 0.0):
img = filt_tanl(img, fl, aa)
img = fft(filt_table(img, table))
img.write_image(out_stack, i)
elif options.rotpw != None:
if len(args) != 1:
ERROR("Only one input permitted", "rotpw", 1)
return
from utilities import write_text_file, get_im
from fundamentals import rops_table
from math import log10
t = rops_table(get_im(args[0]))
x = range(len(t))
r = [0.0] * len(x)
for i in x:
r[i] = log10(t[i])
write_text_file([t, r, x], options.rotpw)
elif options.transformparams != None:
if len(args) != 2:
ERROR(
"Please provide names of input and output files with orientation parameters",
"transformparams", 1)
return
from utilities import read_text_row, write_text_row
transf = [0.0] * 6
spl = options.transformparams.split(',')
for i in xrange(len(spl)):
transf[i] = float(spl[i])
write_text_row(rotate_shift_params(read_text_row(args[0]), transf),
args[1])
elif options.makedb != None:
nargs = len(args)
if nargs != 1:
print "must provide exactly one argument denoting database key under which the input params will be stored"
return
dbkey = args[0]
print "database key under which params will be stored: ", dbkey
gbdb = js_open_dict("e2boxercache/gauss_box_DB.json")
parmstr = 'dummy:' + options.makedb[0]
(processorname, param_dict) = parsemodopt(parmstr)
dbdict = {}
for pkey in param_dict:
if (pkey == 'invert_contrast') or (pkey == 'use_variance'):
if param_dict[pkey] == 'True':
dbdict[pkey] = True
else:
dbdict[pkey] = False
else:
dbdict[pkey] = param_dict[pkey]
gbdb[dbkey] = dbdict
elif options.generate_projections:
nargs = len(args)
if nargs != 3:
ERROR("Must provide name of input structure(s) from which to generate projections, name of output projection stack, and prefix for output micrographs."\
"sxprocess - generate projections",1)
return
inpstr = args[0]
outstk = args[1]
micpref = args[2]
parmstr = 'dummy:' + options.generate_projections[0]
(processorname, param_dict) = parsemodopt(parmstr)
parm_CTF = False
parm_format = 'bdb'
parm_apix = 2.5
if 'CTF' in param_dict:
if param_dict['CTF'] == 'True':
parm_CTF = True
if 'format' in param_dict:
parm_format = param_dict['format']
if 'apix' in param_dict:
parm_apix = float(param_dict['apix'])
boxsize = 64
if 'boxsize' in param_dict:
boxsize = int(param_dict['boxsize'])
print "pixel size: ", parm_apix, " format: ", parm_format, " add CTF: ", parm_CTF, " box size: ", boxsize
scale_mult = 2500
sigma_add = 1.5
sigma_proj = 30.0
sigma2_proj = 17.5
sigma_gauss = 0.3
sigma_mic = 30.0
sigma2_mic = 17.5
sigma_gauss_mic = 0.3
if 'scale_mult' in param_dict:
scale_mult = float(param_dict['scale_mult'])
if 'sigma_add' in param_dict:
sigma_add = float(param_dict['sigma_add'])
if 'sigma_proj' in param_dict:
sigma_proj = float(param_dict['sigma_proj'])
if 'sigma2_proj' in param_dict:
sigma2_proj = float(param_dict['sigma2_proj'])
if 'sigma_gauss' in param_dict:
sigma_gauss = float(param_dict['sigma_gauss'])
if 'sigma_mic' in param_dict:
sigma_mic = float(param_dict['sigma_mic'])
if 'sigma2_mic' in param_dict:
sigma2_mic = float(param_dict['sigma2_mic'])
if 'sigma_gauss_mic' in param_dict:
sigma_gauss_mic = float(param_dict['sigma_gauss_mic'])
from filter import filt_gaussl, filt_ctf
from utilities import drop_spider_doc, even_angles, model_gauss, delete_bdb, model_blank, pad, model_gauss_noise, set_params2D, set_params_proj
from projection import prep_vol, prgs
seed(14567)
delta = 29
angles = even_angles(delta, 0.0, 89.9, 0.0, 359.9, "S")
nangle = len(angles)
modelvol = []
nvlms = EMUtil.get_image_count(inpstr)
from utilities import get_im
for k in xrange(nvlms):
modelvol.append(get_im(inpstr, k))
nx = modelvol[0].get_xsize()
if nx != boxsize:
ERROR("Requested box dimension does not match dimension of the input model.", \
"sxprocess - generate projections",1)
nvol = 10
volfts = [[] for k in xrange(nvlms)]
for k in xrange(nvlms):
for i in xrange(nvol):
sigma = sigma_add + random() # 1.5-2.5
addon = model_gauss(sigma, boxsize, boxsize, boxsize, sigma,
sigma, 38, 38, 40)
scale = scale_mult * (0.5 + random())
vf, kb = prep_vol(modelvol[k] + scale * addon)
volfts[k].append(vf)
del vf, modelvol
if parm_format == "bdb":
stack_data = "bdb:" + outstk
delete_bdb(stack_data)
else:
stack_data = outstk + ".hdf"
Cs = 2.0
pixel = parm_apix
voltage = 120.0
ampcont = 10.0
ibd = 4096 / 2 - boxsize
iprj = 0
width = 240
xstart = 8 + boxsize / 2
ystart = 8 + boxsize / 2
rowlen = 17
from random import randint
params = []
for idef in xrange(3, 8):
irow = 0
icol = 0
mic = model_blank(4096, 4096)
defocus = idef * 0.5 #0.2
if parm_CTF:
astampl = defocus * 0.15
astangl = 50.0
ctf = generate_ctf([
defocus, Cs, voltage, pixel, ampcont, 0.0, astampl, astangl
])
for i in xrange(nangle):
for k in xrange(12):
dphi = 8.0 * (random() - 0.5)
dtht = 8.0 * (random() - 0.5)
psi = 360.0 * random()
phi = angles[i][0] + dphi
tht = angles[i][1] + dtht
s2x = 4.0 * (random() - 0.5)
s2y = 4.0 * (random() - 0.5)
params.append([phi, tht, psi, s2x, s2y])
ivol = iprj % nvol
#imgsrc = randint(0,nvlms-1)
imgsrc = iprj % nvlms
proj = prgs(volfts[imgsrc][ivol], kb,
[phi, tht, psi, -s2x, -s2y])
x = xstart + irow * width
y = ystart + icol * width
mic += pad(proj, 4096, 4096, 1, 0.0, x - 2048, y - 2048, 0)
proj = proj + model_gauss_noise(sigma_proj, nx, nx)
if parm_CTF:
proj = filt_ctf(proj, ctf)
proj.set_attr_dict({"ctf": ctf, "ctf_applied": 0})
proj = proj + filt_gaussl(
model_gauss_noise(sigma2_proj, nx, nx), sigma_gauss)
proj.set_attr("origimgsrc", imgsrc)
proj.set_attr("test_id", iprj)
# flags describing the status of the image (1 = true, 0 = false)
set_params2D(proj, [0.0, 0.0, 0.0, 0, 1.0])
set_params_proj(proj, [phi, tht, psi, s2x, s2y])
proj.write_image(stack_data, iprj)
icol += 1
if icol == rowlen:
icol = 0
irow += 1
iprj += 1
mic += model_gauss_noise(sigma_mic, 4096, 4096)
if parm_CTF:
#apply CTF
mic = filt_ctf(mic, ctf)
mic += filt_gaussl(model_gauss_noise(sigma2_mic, 4096, 4096),
sigma_gauss_mic)
mic.write_image(micpref + "%1d.hdf" % (idef - 3), 0)
drop_spider_doc("params.txt", params)
elif options.importctf != None:
print ' IMPORTCTF '
from utilities import read_text_row, write_text_row
from random import randint
import subprocess
grpfile = 'groupid%04d' % randint(1000, 9999)
ctfpfile = 'ctfpfile%04d' % randint(1000, 9999)
cterr = [options.defocuserror / 100.0, options.astigmatismerror]
ctfs = read_text_row(options.importctf)
for kk in xrange(len(ctfs)):
root, name = os.path.split(ctfs[kk][-1])
ctfs[kk][-1] = name[:-4]
if (options.input[:4] != 'bdb:'):
ERROR('Sorry, only bdb files implemented', 'importctf', 1)
d = options.input[4:]
#try: str = d.index('*')
#except: str = -1
from string import split
import glob
uu = os.path.split(d)
uu = os.path.join(uu[0], 'EMAN2DB', uu[1] + '.bdb')
flist = glob.glob(uu)
for i in xrange(len(flist)):
root, name = os.path.split(flist[i])
root = root[:-7]
name = name[:-4]
fil = 'bdb:' + os.path.join(root, name)
sourcemic = EMUtil.get_all_attributes(fil, 'ptcl_source_image')
nn = len(sourcemic)
gctfp = []
groupid = []
for kk in xrange(nn):
junk, name2 = os.path.split(sourcemic[kk])
name2 = name2[:-4]
ctfp = [-1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
for ll in xrange(len(ctfs)):
if (name2 == ctfs[ll][-1]):
# found correct
if (ctfs[ll][8] / ctfs[ll][0] <= cterr[0]):
# acceptable defocus error
ctfp = ctfs[ll][:8]
if (ctfs[ll][10] > cterr[1]):
# error of astigmatism exceed the threshold, set astigmatism to zero.
ctfp[6] = 0.0
ctfp[7] = 0.0
gctfp.append(ctfp)
groupid.append(kk)
break
if (len(groupid) > 0):
write_text_row(groupid, grpfile)
write_text_row(gctfp, ctfpfile)
cmd = "{} {} {} {}".format(
'e2bdb.py', fil, '--makevstack=bdb:' + root + 'G' + name,
'--list=' + grpfile)
#print cmd
subprocess.call(cmd, shell=True)
cmd = "{} {} {} {}".format('sxheader.py',
'bdb:' + root + 'G' + name,
'--params=ctf',
'--import=' + ctfpfile)
#print cmd
subprocess.call(cmd, shell=True)
else:
print ' >>> Group ', name, ' skipped.'
cmd = "{} {} {}".format("rm -f", grpfile, ctfpfile)
subprocess.call(cmd, shell=True)
elif options.scale > 0.0:
from utilities import read_text_row, write_text_row
scale = options.scale
nargs = len(args)
if nargs != 2:
print "Please provide names of input and output file!"
return
p = read_text_row(args[0])
for i in xrange(len(p)):
p[i][3] /= scale
p[i][4] /= scale
write_text_row(p, args[1])
elif options.adaptive_mask:
from utilities import get_im
from morphology import adaptive_mask
nsigma = options.nsigma
ndilation = options.ndilation
kernel_size = options.kernel_size
gauss_standard_dev = options.gauss_standard_dev
nargs = len(args)
if nargs > 2:
print "Too many inputs are given, try again!"
return
else:
inputvol = get_im(args[0])
input_path, input_file_name = os.path.split(args[0])
input_file_name_root, ext = os.path.splitext(input_file_name)
if nargs == 2: mask_file_name = args[1]
else:
mask_file_name = "adaptive_mask_for" + input_file_name_root + ".hdf" # Only hdf file is output.
mask3d = adaptive_mask(inputvol, nsigma, ndilation, kernel_size,
gauss_standard_dev)
mask3d.write_image(mask_file_name)
else:
ERROR("Please provide option name", "sxprocess.py", 1)
def helicalshiftali_MPI(stack, maskfile=None, maxit=100, CTF=False, snr=1.0, Fourvar=False, search_rng=-1):
from applications import MPI_start_end
from utilities import model_circle, model_blank, get_image, peak_search, get_im, pad
from utilities import reduce_EMData_to_root, bcast_EMData_to_all, send_attr_dict, file_type, bcast_number_to_all, bcast_list_to_all
from statistics import varf2d_MPI
from fundamentals import fft, ccf, rot_shift3D, rot_shift2D, fshift
from utilities import get_params2D, set_params2D, chunks_distribution
from utilities import print_msg, print_begin_msg, print_end_msg
import os
import sys
from mpi import mpi_init, mpi_comm_size, mpi_comm_rank, MPI_COMM_WORLD
from mpi import mpi_reduce, mpi_bcast, mpi_barrier, mpi_gatherv
from mpi import MPI_SUM, MPI_FLOAT, MPI_INT
from time import time
from pixel_error import ordersegments
from math import sqrt, atan2, tan, pi
nproc = mpi_comm_size(MPI_COMM_WORLD)
myid = mpi_comm_rank(MPI_COMM_WORLD)
main_node = 0
ftp = file_type(stack)
if myid == main_node:
print_begin_msg("helical-shiftali_MPI")
max_iter=int(maxit)
if( myid == main_node):
infils = EMUtil.get_all_attributes(stack, "filament")
ptlcoords = EMUtil.get_all_attributes(stack, 'ptcl_source_coord')
filaments = ordersegments(infils, ptlcoords)
total_nfils = len(filaments)
inidl = [0]*total_nfils
for i in xrange(total_nfils): inidl[i] = len(filaments[i])
linidl = sum(inidl)
nima = linidl
tfilaments = []
for i in xrange(total_nfils): tfilaments += filaments[i]
del filaments
else:
total_nfils = 0
linidl = 0
total_nfils = bcast_number_to_all(total_nfils, source_node = main_node)
if myid != main_node:
inidl = [-1]*total_nfils
inidl = bcast_list_to_all(inidl, myid, source_node = main_node)
linidl = bcast_number_to_all(linidl, source_node = main_node)
if myid != main_node:
tfilaments = [-1]*linidl
tfilaments = bcast_list_to_all(tfilaments, myid, source_node = main_node)
filaments = []
iendi = 0
for i in xrange(total_nfils):
isti = iendi
iendi = isti+inidl[i]
filaments.append(tfilaments[isti:iendi])
del tfilaments,inidl
if myid == main_node:
print_msg( "total number of filaments: %d"%total_nfils)
if total_nfils< nproc:
ERROR('number of CPUs (%i) is larger than the number of filaments (%i), please reduce the number of CPUs used'%(nproc, total_nfils), "ehelix_MPI", 1,myid)
# balanced load
temp = chunks_distribution([[len(filaments[i]), i] for i in xrange(len(filaments))], nproc)[myid:myid+1][0]
filaments = [filaments[temp[i][1]] for i in xrange(len(temp))]
nfils = len(filaments)
#filaments = [[0,1]]
#print "filaments",filaments
list_of_particles = []
indcs = []
k = 0
for i in xrange(nfils):
list_of_particles += filaments[i]
k1 = k+len(filaments[i])
indcs.append([k,k1])
k = k1
data = EMData.read_images(stack, list_of_particles)
ldata = len(data)
print "ldata=", ldata
nx = data[0].get_xsize()
ny = data[0].get_ysize()
if maskfile == None:
mrad = min(nx, ny)//2-2
mask = pad( model_blank(2*mrad+1, ny, 1, 1.0), nx, ny, 1, 0.0)
else:
mask = get_im(maskfile)
# apply initial xform.align2d parameters stored in header
init_params = []
for im in xrange(ldata):
t = data[im].get_attr('xform.align2d')
init_params.append(t)
p = t.get_params("2d")
data[im] = rot_shift2D(data[im], p['alpha'], p['tx'], p['ty'], p['mirror'], p['scale'])
if CTF:
from filter import filt_ctf
from morphology import ctf_img
ctf_abs_sum = EMData(nx, ny, 1, False)
ctf_2_sum = EMData(nx, ny, 1, False)
else:
ctf_2_sum = None
ctf_abs_sum = None
from utilities import info
for im in xrange(ldata):
data[im].set_attr('ID', list_of_particles[im])
st = Util.infomask(data[im], mask, False)
data[im] -= st[0]
if CTF:
ctf_params = data[im].get_attr("ctf")
qctf = data[im].get_attr("ctf_applied")
if qctf == 0:
data[im] = filt_ctf(fft(data[im]), ctf_params)
data[im].set_attr('ctf_applied', 1)
elif qctf != 1:
ERROR('Incorrectly set qctf flag', "helicalshiftali_MPI", 1,myid)
ctfimg = ctf_img(nx, ctf_params, ny=ny)
Util.add_img2(ctf_2_sum, ctfimg)
Util.add_img_abs(ctf_abs_sum, ctfimg)
else: data[im] = fft(data[im])
del list_of_particles
if CTF:
reduce_EMData_to_root(ctf_2_sum, myid, main_node)
reduce_EMData_to_root(ctf_abs_sum, myid, main_node)
if CTF:
if myid != main_node:
del ctf_2_sum
del ctf_abs_sum
else:
temp = EMData(nx, ny, 1, False)
tsnr = 1./snr
for i in xrange(0,nx+2,2):
for j in xrange(ny):
temp.set_value_at(i,j,tsnr)
temp.set_value_at(i+1,j,0.0)
#info(ctf_2_sum)
Util.add_img(ctf_2_sum, temp)
#info(ctf_2_sum)
del temp
total_iter = 0
shift_x = [0.0]*ldata
for Iter in xrange(max_iter):
if myid == main_node:
start_time = time()
print_msg("Iteration #%4d\n"%(total_iter))
total_iter += 1
avg = EMData(nx, ny, 1, False)
for im in xrange(ldata):
Util.add_img(avg, fshift(data[im], shift_x[im]))
reduce_EMData_to_root(avg, myid, main_node)
if myid == main_node:
if CTF: tavg = Util.divn_filter(avg, ctf_2_sum)
else: tavg = Util.mult_scalar(avg, 1.0/float(nima))
else:
tavg = model_blank(nx,ny)
if Fourvar:
bcast_EMData_to_all(tavg, myid, main_node)
vav, rvar = varf2d_MPI(myid, data, tavg, mask, "a", CTF)
if myid == main_node:
if Fourvar:
tavg = fft(Util.divn_img(fft(tavg), vav))
vav_r = Util.pack_complex_to_real(vav)
# normalize and mask tavg in real space
tavg = fft(tavg)
stat = Util.infomask( tavg, mask, False )
tavg -= stat[0]
Util.mul_img(tavg, mask)
tavg.write_image("tavg.hdf",Iter)
# For testing purposes: shift tavg to some random place and see if the centering is still correct
#tavg = rot_shift3D(tavg,sx=3,sy=-4)
if Fourvar: del vav
bcast_EMData_to_all(tavg, myid, main_node)
tavg = fft(tavg)
sx_sum = 0.0
nxc = nx//2
for ifil in xrange(nfils):
"""
# Calculate filament average
avg = EMData(nx, ny, 1, False)
filnima = 0
for im in xrange(indcs[ifil][0], indcs[ifil][1]):
Util.add_img(avg, data[im])
filnima += 1
tavg = Util.mult_scalar(avg, 1.0/float(filnima))
"""
# Calculate 1D ccf between each segment and filament average
nsegms = indcs[ifil][1]-indcs[ifil][0]
ctx = [None]*nsegms
pcoords = [None]*nsegms
for im in xrange(indcs[ifil][0], indcs[ifil][1]):
ctx[im-indcs[ifil][0]] = Util.window(ccf(tavg, data[im]), nx, 1)
pcoords[im-indcs[ifil][0]] = data[im].get_attr('ptcl_source_coord')
#ctx[im-indcs[ifil][0]].write_image("ctx.hdf",im-indcs[ifil][0])
#print " CTX ",myid,im,Util.infomask(ctx[im-indcs[ifil][0]], None, True)
# search for best x-shift
cents = nsegms//2
dst = sqrt(max((pcoords[cents][0] - pcoords[0][0])**2 + (pcoords[cents][1] - pcoords[0][1])**2, (pcoords[cents][0] - pcoords[-1][0])**2 + (pcoords[cents][1] - pcoords[-1][1])**2))
maxincline = atan2(ny//2-2-float(search_rng),dst)
kang = int(dst*tan(maxincline)+0.5)
#print " settings ",nsegms,cents,dst,search_rng,maxincline,kang
# ## C code for alignment. @ming
results = [0.0]*3;
results = Util.helixshiftali(ctx, pcoords, nsegms, maxincline, kang, search_rng,nxc)
sib = int(results[0])
bang = results[1]
qm = results[2]
#print qm, sib, bang
# qm = -1.e23
#
# for six in xrange(-search_rng, search_rng+1,1):
# q0 = ctx[cents].get_value_at(six+nxc)
# for incline in xrange(kang+1):
# qt = q0
# qu = q0
# if(kang>0): tang = tan(maxincline/kang*incline)
# else: tang = 0.0
# for kim in xrange(cents+1,nsegms):
# dst = sqrt((pcoords[cents][0] - pcoords[kim][0])**2 + (pcoords[cents][1] - pcoords[kim][1])**2)
# xl = dst*tang+six+nxc
# ixl = int(xl)
# dxl = xl - ixl
# #print " A ", ifil,six,incline,kim,xl,ixl,dxl
# qt += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# xl = -dst*tang+six+nxc
# ixl = int(xl)
# dxl = xl - ixl
# qu += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# for kim in xrange(cents):
# dst = sqrt((pcoords[cents][0] - pcoords[kim][0])**2 + (pcoords[cents][1] - pcoords[kim][1])**2)
# xl = -dst*tang+six+nxc
# ixl = int(xl)
# dxl = xl - ixl
# qt += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# xl = dst*tang+six+nxc
# ixl = int(xl)
# dxl = xl - ixl
# qu += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# if( qt > qm ):
# qm = qt
# sib = six
# bang = tang
# if( qu > qm ):
# qm = qu
# sib = six
# bang = -tang
#if incline == 0: print "incline = 0 ",six,tang,qt,qu
#print qm,six,sib,bang
#print " got results ",indcs[ifil][0], indcs[ifil][1], ifil,myid,qm,sib,tang,bang,len(ctx),Util.infomask(ctx[0], None, True)
for im in xrange(indcs[ifil][0], indcs[ifil][1]):
kim = im-indcs[ifil][0]
dst = sqrt((pcoords[cents][0] - pcoords[kim][0])**2 + (pcoords[cents][1] - pcoords[kim][1])**2)
if(kim < cents): xl = -dst*bang+sib
else: xl = dst*bang+sib
shift_x[im] = xl
# Average shift
sx_sum += shift_x[indcs[ifil][0]+cents]
# #print myid,sx_sum,total_nfils
sx_sum = mpi_reduce(sx_sum, 1, MPI_FLOAT, MPI_SUM, main_node, MPI_COMM_WORLD)
if myid == main_node:
sx_sum = float(sx_sum[0])/total_nfils
print_msg("Average shift %6.2f\n"%(sx_sum))
else:
sx_sum = 0.0
sx_sum = 0.0
sx_sum = bcast_number_to_all(sx_sum, source_node = main_node)
for im in xrange(ldata):
shift_x[im] -= sx_sum
#print " %3d %6.3f"%(im,shift_x[im])
#exit()
# combine shifts found with the original parameters
for im in xrange(ldata):
t1 = Transform()
##import random
##shix=random.randint(-10, 10)
##t1.set_params({"type":"2D","tx":shix})
t1.set_params({"type":"2D","tx":shift_x[im]})
# combine t0 and t1
tt = t1*init_params[im]
data[im].set_attr("xform.align2d", tt)
# write out headers and STOP, under MPI writing has to be done sequentially
mpi_barrier(MPI_COMM_WORLD)
par_str = ["xform.align2d", "ID"]
if myid == main_node:
from utilities import file_type
if(file_type(stack) == "bdb"):
from utilities import recv_attr_dict_bdb
recv_attr_dict_bdb(main_node, stack, data, par_str, 0, ldata, nproc)
else:
from utilities import recv_attr_dict
recv_attr_dict(main_node, stack, data, par_str, 0, ldata, nproc)
else: send_attr_dict(main_node, data, par_str, 0, ldata)
if myid == main_node: print_end_msg("helical-shiftali_MPI")
def main():
from optparse import OptionParser
from global_def import SPARXVERSION
from EMAN2 import EMData
from logger import Logger, BaseLogger_Files
import sys, os, time
global Tracker, Blockdata
from global_def import ERROR
progname = os.path.basename(sys.argv[0])
usage = progname + " --output_dir=output_dir --isac_dir=output_dir_of_isac "
parser = OptionParser(usage, version=SPARXVERSION)
parser.add_option(
"--adjust_to_analytic_model",
action="store_true",
default=False,
help="adjust power spectrum of 2-D averages to an analytic model ")
parser.add_option(
"--adjust_to_given_pw2",
action="store_true",
default=False,
help="adjust power spectrum to 2-D averages to given 1D power spectrum"
)
parser.add_option("--B_enhance",
action="store_true",
default=False,
help="using B-factor to enhance 2-D averages")
parser.add_option("--no_adjustment",
action="store_true",
default=False,
help="No power spectrum adjustment")
options_list = []
adjust_to_analytic_model = False
for q in sys.argv[1:]:
if (q[:26] == "--adjust_to_analytic_model"):
adjust_to_analytic_model = True
options_list.append(q)
break
adjust_to_given_pw2 = False
for q in sys.argv[1:]:
if (q[:21] == "--adjust_to_given_pw2"):
adjust_to_given_pw2 = True
options_list.append(q)
break
B_enhance = False
for q in sys.argv[1:]:
if (q[:11] == "--B_enhance"):
B_enhance = True
options_list.append(q)
break
no_adjustment = False
for q in sys.argv[1:]:
if (q[:15] == "--no_adjustment"):
no_adjustment = True
options_list.append(q)
break
if len(options_list) == 0:
if (Blockdata["myid"] == Blockdata["main_node"]):
print(
"specify one of the following options to start: 1. adjust_to_analytic_model; 2. adjust_to_given_pw2; 3. B_enhance; 4. no_adjustment"
)
if len(options_list) > 1:
ERROR(
"The specified options are exclusive. Use only one of them to start",
"sxcompute_isac_avg.py", 1, Blockdata["myid"])
# options in common
parser.add_option(
"--isac_dir",
type="string",
default='',
help="ISAC run output directory, input directory for this command")
parser.add_option(
"--output_dir",
type="string",
default='',
help="output directory where computed averages are saved")
parser.add_option("--pixel_size",
type="float",
default=-1.0,
help="pixel_size of raw images")
parser.add_option(
"--fl",
type="float",
default=-1.0,
help=
"low pass filter, =-1, not applied; =1, using FH1 (initial resolution), =2 using FH2 (resolution after local alignment), or user provided value"
)
parser.add_option("--stack",
type="string",
default="",
help="data stack used in ISAC")
parser.add_option("--radius", type="int", default=-1, help="radius")
parser.add_option("--xr",
type="float",
default=-1.0,
help="local alignment search range")
parser.add_option("--ts",
type="float",
default=1.0,
help="local alignment search step")
parser.add_option("--fh",
type="float",
default=-1.,
help="local alignment high frequencies limit")
parser.add_option("--maxit",
type="int",
default=5,
help="local alignment iterations")
parser.add_option("--navg",
type="int",
default=-1,
help="number of aveages")
parser.add_option("--skip_local_alignment",
action="store_true",
default=False,
help="skip local alignment")
parser.add_option(
"--noctf",
action="store_true",
default=False,
help=
"no ctf correction, useful for negative stained data. always ctf for cryo data"
)
if B_enhance:
parser.add_option(
"--B_start",
type="float",
default=10.0,
help=
"start frequency (1./Angstrom) of power spectrum for B_factor estimation"
)
parser.add_option(
"--Bfactor",
type="float",
default=-1.0,
help=
"User defined bactors (e.g. 45.0[A^2]). By default, the program automatically estimates B-factor. "
)
if adjust_to_given_pw2:
parser.add_option("--modelpw",
type="string",
default='',
help="1-D reference power spectrum")
checking_flag = 0
if (Blockdata["myid"] == Blockdata["main_node"]):
if not os.path.exists(options.modelpw): checking_flag = 1
checking_flag = bcast_number_to_all(checking_flag,
Blockdata["main_node"],
MPI_COMM_WORLD)
if checking_flag == 1:
ERROR("User provided power spectrum does not exist",
"sxcompute_isac_avg.py", 1, Blockdata["myid"])
(options, args) = parser.parse_args(sys.argv[1:])
Tracker = {}
Constants = {}
Constants["isac_dir"] = options.isac_dir
Constants["masterdir"] = options.output_dir
Constants["pixel_size"] = options.pixel_size
Constants["orgstack"] = options.stack
Constants["radius"] = options.radius
Constants["xrange"] = options.xr
Constants["xstep"] = options.ts
Constants["FH"] = options.fh
Constants["maxit"] = options.maxit
Constants["navg"] = options.navg
Constants["low_pass_filter"] = options.fl
if B_enhance:
Constants["B_start"] = options.B_start
Constants["Bfactor"] = options.Bfactor
if adjust_to_given_pw2: Constants["modelpw"] = options.modelpw
Tracker["constants"] = Constants
# -------------------------------------------------------------
#
# Create and initialize Tracker dictionary with input options # State Variables
#<<<---------------------->>>imported functions<<<---------------------------------------------
from utilities import get_im, bcast_number_to_all, write_text_file, read_text_file, wrap_mpi_bcast, write_text_row
from utilities import cmdexecute
from filter import filt_tanl
from time import sleep
from logger import Logger, BaseLogger_Files
import user_functions
import string
from string import split, atoi, atof
import json
#x_range = max(Tracker["constants"]["xrange"], int(1./Tracker["ini_shrink"])+1)
#y_range = x_range
####-----------------------------------------------------------
# Create Master directory
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
if Tracker["constants"]["masterdir"] == Tracker["constants"]["isac_dir"]:
masterdir = os.path.join(Tracker["constants"]["isac_dir"], "sharpen")
else:
masterdir = Tracker["constants"]["masterdir"]
if (Blockdata["myid"] == Blockdata["main_node"]):
msg = "Postprocessing ISAC 2D averages starts"
print(line, "Postprocessing ISAC 2D averages starts")
if not masterdir:
timestring = strftime("_%d_%b_%Y_%H_%M_%S", localtime())
masterdir = "sharpen_" + Tracker["constants"]["isac_dir"]
os.mkdir(masterdir)
else:
if os.path.exists(masterdir):
print("%s already exists" % masterdir)
else:
os.mkdir(masterdir)
li = len(masterdir)
else:
li = 0
li = mpi_bcast(li, 1, MPI_INT, Blockdata["main_node"], MPI_COMM_WORLD)[0]
masterdir = mpi_bcast(masterdir, li, MPI_CHAR, Blockdata["main_node"],
MPI_COMM_WORLD)
masterdir = string.join(masterdir, "")
Tracker["constants"]["masterdir"] = masterdir
log_main = Logger(BaseLogger_Files())
log_main.prefix = Tracker["constants"]["masterdir"] + "/"
while not os.path.exists(Tracker["constants"]["masterdir"]):
print("Node ", Blockdata["myid"], " waiting...",
Tracker["constants"]["masterdir"])
sleep(1)
mpi_barrier(MPI_COMM_WORLD)
if (Blockdata["myid"] == Blockdata["main_node"]):
init_dict = {}
print(Tracker["constants"]["isac_dir"])
Tracker["directory"] = os.path.join(Tracker["constants"]["isac_dir"],
"2dalignment")
core = read_text_row(
os.path.join(Tracker["directory"], "initial2Dparams.txt"))
for im in xrange(len(core)):
init_dict[im] = core[im]
del core
else:
init_dict = 0
init_dict = wrap_mpi_bcast(init_dict,
Blockdata["main_node"],
communicator=MPI_COMM_WORLD)
###
if (Blockdata["myid"] == Blockdata["main_node"]):
#Tracker["constants"]["orgstack"] = "bdb:"+ os.path.join(Tracker["constants"]["isac_dir"],"../","sparx_stack")
image = get_im(Tracker["constants"]["orgstack"], 0)
Tracker["constants"]["nnxo"] = image.get_xsize()
try:
ctf_params = image.get_attr("ctf")
if Tracker["constants"]["pixel_size"] == -1.:
Tracker["constants"]["pixel_size"] = ctf_params.apix
except:
print("pixel size value is not given.")
Tracker["ini_shrink"] = float(
get_im(os.path.join(Tracker["directory"], "aqfinal.hdf"),
0).get_xsize()) / Tracker["constants"]["nnxo"]
else:
Tracker["ini_shrink"] = 0
Tracker = wrap_mpi_bcast(Tracker,
Blockdata["main_node"],
communicator=MPI_COMM_WORLD)
#print(Tracker["constants"]["pixel_size"], "pixel_size")
x_range = max(Tracker["constants"]["xrange"],
int(1. / Tracker["ini_shrink"]) + 1)
y_range = x_range
if (Blockdata["myid"] == Blockdata["main_node"]):
parameters = read_text_row(
os.path.join(Tracker["constants"]["isac_dir"],
"all_parameters.txt"))
else:
parameters = 0
parameters = wrap_mpi_bcast(parameters,
Blockdata["main_node"],
communicator=MPI_COMM_WORLD)
params_dict = {}
list_dict = {}
#parepare params_dict
if Tracker["constants"]["navg"] < 0:
navg = EMUtil.get_image_count(
os.path.join(Tracker["constants"]["isac_dir"],
"class_averages.hdf"))
else:
navg = min(
Tracker["constants"]["navg"],
EMUtil.get_image_count(
os.path.join(Tracker["constants"]["isac_dir"],
"class_averages.hdf")))
global_dict = {}
ptl_list = []
memlist = []
if (Blockdata["myid"] == Blockdata["main_node"]):
for iavg in xrange(navg):
params_of_this_average = []
image = get_im(
os.path.join(Tracker["constants"]["isac_dir"],
"class_averages.hdf"), iavg)
members = image.get_attr("members")
memlist.append(members)
for im in xrange(len(members)):
abs_id = members[im]
global_dict[abs_id] = [iavg, im]
P = combine_params2( init_dict[abs_id][0], init_dict[abs_id][1], init_dict[abs_id][2], init_dict[abs_id][3], \
parameters[abs_id][0], parameters[abs_id][1]/Tracker["ini_shrink"], parameters[abs_id][2]/Tracker["ini_shrink"], parameters[abs_id][3])
if parameters[abs_id][3] == -1: print("wrong one")
params_of_this_average.append([P[0], P[1], P[2], P[3], 1.0])
ptl_list.append(abs_id)
params_dict[iavg] = params_of_this_average
list_dict[iavg] = members
write_text_row(
params_of_this_average,
os.path.join(Tracker["constants"]["masterdir"],
"params_avg_%03d.txt" % iavg))
ptl_list.sort()
init_params = [None for im in xrange(len(ptl_list))]
for im in xrange(len(ptl_list)):
init_params[im] = [ptl_list[im]] + params_dict[global_dict[
ptl_list[im]][0]][global_dict[ptl_list[im]][1]]
write_text_row(
init_params,
os.path.join(Tracker["constants"]["masterdir"],
"init_isac_params.txt"))
else:
params_dict = 0
list_dict = 0
memlist = 0
params_dict = wrap_mpi_bcast(params_dict,
Blockdata["main_node"],
communicator=MPI_COMM_WORLD)
list_dict = wrap_mpi_bcast(list_dict,
Blockdata["main_node"],
communicator=MPI_COMM_WORLD)
memlist = wrap_mpi_bcast(memlist,
Blockdata["main_node"],
communicator=MPI_COMM_WORLD)
# Now computing!
del init_dict
tag_sharpen_avg = 1000
## always apply low pass filter to B_enhanced images to suppress noise in high frequencies
enforced_to_H1 = False
if options.B_enhance:
if Tracker["constants"]["low_pass_filter"] == -1:
print("User does not provide low pass filter")
enforced_to_H1 = True
if navg < Blockdata["nproc"]: # Each CPU do one average
FH_list = [None for im in xrange(navg)]
for iavg in xrange(navg):
if Blockdata["myid"] == iavg:
mlist = [None for i in xrange(len(list_dict[iavg]))]
for im in xrange(len(mlist)):
mlist[im] = get_im(Tracker["constants"]["orgstack"],
list_dict[iavg][im])
set_params2D(mlist[im],
params_dict[iavg][im],
xform="xform.align2d")
if options.noctf:
new_avg, frc, plist = compute_average_noctf(
mlist, Tracker["constants"]["radius"])
else:
new_avg, frc, plist = compute_average_ctf(
mlist, Tracker["constants"]["radius"])
FH1 = get_optimistic_res(frc)
#write_text_file(frc, os.path.join(Tracker["constants"]["masterdir"], "fsc%03d_before_ali.txt"%iavg))
if not options.skip_local_alignment:
new_average1 = within_group_refinement([mlist[kik] for kik in xrange(0,len(mlist),2)], maskfile= None, randomize= False, ir=1.0, \
ou=Tracker["constants"]["radius"], rs=1.0, xrng=[x_range], yrng=[y_range], step=[Tracker["constants"]["xstep"]], \
dst=0.0, maxit=Tracker["constants"]["maxit"], FH = max(Tracker["constants"]["FH"], FH1), FF=0.1)
new_average2 = within_group_refinement([mlist[kik] for kik in xrange(1,len(mlist),2)], maskfile= None, randomize= False, ir=1.0, \
ou=Tracker["constants"]["radius"], rs=1.0, xrng=[x_range], yrng=[y_range], step=[Tracker["constants"]["xstep"]], \
dst=0.0, maxit=Tracker["constants"]["maxit"], FH = max(Tracker["constants"]["FH"], FH1), FF=0.1)
if options.noctf:
new_avg, frc, plist = compute_average_noctf(
mlist, Tracker["constants"]["radius"])
else:
new_avg, frc, plist = compute_average_ctf(
mlist, Tracker["constants"]["radius"])
FH2 = get_optimistic_res(frc)
#write_text_file(frc, os.path.join(Tracker["constants"]["masterdir"], "fsc%03d.txt"%iavg))
#if Tracker["constants"]["nopwadj"]: # pw adjustment, 1. analytic model 2. PDB model 3. B-facttor enhancement
else:
FH2 = 0.0
FH_list[iavg] = [FH1, FH2]
if options.B_enhance:
new_avg, gb = apply_enhancement(
new_avg, Tracker["constants"]["B_start"],
Tracker["constants"]["pixel_size"],
Tracker["constants"]["Bfactor"])
print("Process avg %d %f %f %f" %
(iavg, gb, FH1, FH2))
elif options.adjust_to_given_pw2:
roo = read_text_file(Tracker["constants"]["modelpw"], -1)
roo = roo[0] # always put pw in the first column
new_avg = adjust_pw_to_model(
new_avg, Tracker["constants"]["pixel_size"], roo)
elif options.adjust_to_analytic_model:
new_avg = adjust_pw_to_model(
new_avg, Tracker["constants"]["pixel_size"], None)
elif options.no_adjustment:
pass
print("Process avg %d %f %f" % (iavg, FH1, FH2))
if Tracker["constants"]["low_pass_filter"] != -1.:
if Tracker["constants"]["low_pass_filter"] == 1.:
low_pass_filter = FH1
elif Tracker["constants"]["low_pass_filter"] == 2.:
low_pass_filter = FH2
if options.skip_local_alignment: low_pass_filter = FH1
else:
low_pass_filter = Tracker["constants"][
"low_pass_filter"]
if low_pass_filter >= 0.45: low_pass_filter = 0.45
new_avg = filt_tanl(new_avg, low_pass_filter, 0.1)
new_avg.set_attr("members", list_dict[iavg])
new_avg.set_attr("n_objects", len(list_dict[iavg]))
mpi_barrier(MPI_COMM_WORLD)
for im in xrange(navg): # avg
if im == Blockdata[
"myid"] and Blockdata["myid"] != Blockdata["main_node"]:
send_EMData(new_avg, Blockdata["main_node"], tag_sharpen_avg)
elif Blockdata["myid"] == Blockdata["main_node"]:
if im != Blockdata["main_node"]:
new_avg_other_cpu = recv_EMData(im, tag_sharpen_avg)
new_avg_other_cpu.set_attr("members", memlist[im])
new_avg_other_cpu.write_image(
os.path.join(Tracker["constants"]["masterdir"],
"class_averages.hdf"), im)
else:
new_avg.write_image(
os.path.join(Tracker["constants"]["masterdir"],
"class_averages.hdf"), im)
if not options.skip_local_alignment:
if im == Blockdata["myid"]:
write_text_row(
plist,
os.path.join(Tracker["constants"]["masterdir"],
"ali2d_local_params_avg_%03d.txt" % im))
if Blockdata["myid"] == im and Blockdata["myid"] != Blockdata[
"main_node"]:
wrap_mpi_send(plist_dict[im], Blockdata["main_node"],
MPI_COMM_WORLD)
elif im != Blockdata["main_node"] and Blockdata[
"myid"] == Blockdata["main_node"]:
dummy = wrap_mpi_recv(im, MPI_COMM_WORLD)
plist_dict[im] = dummy
if im == Blockdata["myid"] and im != Blockdata["main_node"]:
wrap_mpi_send(FH_list[im], Blockdata["main_node"],
MPI_COMM_WORLD)
elif im != Blockdata["main_node"] and Blockdata[
"myid"] == Blockdata["main_node"]:
dummy = wrap_mpi_recv(im, MPI_COMM_WORLD)
FH_list[im] = dummy
else:
if im == Blockdata["myid"] and im != Blockdata["main_node"]:
wrap_mpi_send(FH_list, Blockdata["main_node"],
MPI_COMM_WORLD)
elif im != Blockdata["main_node"] and Blockdata[
"myid"] == Blockdata["main_node"]:
dummy = wrap_mpi_recv(im, MPI_COMM_WORLD)
FH_list[im] = dummy[im]
mpi_barrier(MPI_COMM_WORLD)
else:
FH_list = [[0, 0.0, 0.0] for im in xrange(navg)]
image_start, image_end = MPI_start_end(navg, Blockdata["nproc"],
Blockdata["myid"])
if Blockdata["myid"] == Blockdata["main_node"]:
cpu_dict = {}
for iproc in xrange(Blockdata["nproc"]):
local_image_start, local_image_end = MPI_start_end(
navg, Blockdata["nproc"], iproc)
for im in xrange(local_image_start, local_image_end):
cpu_dict[im] = iproc
else:
cpu_dict = 0
cpu_dict = wrap_mpi_bcast(cpu_dict,
Blockdata["main_node"],
communicator=MPI_COMM_WORLD)
slist = [None for im in xrange(navg)]
ini_list = [None for im in xrange(navg)]
avg1_list = [None for im in xrange(navg)]
avg2_list = [None for im in xrange(navg)]
plist_dict = {}
data_list = [None for im in xrange(navg)]
if Blockdata["myid"] == Blockdata["main_node"]: print("read data")
for iavg in xrange(image_start, image_end):
mlist = [None for i in xrange(len(list_dict[iavg]))]
for im in xrange(len(mlist)):
mlist[im] = get_im(Tracker["constants"]["orgstack"],
list_dict[iavg][im])
set_params2D(mlist[im],
params_dict[iavg][im],
xform="xform.align2d")
data_list[iavg] = mlist
print("read data done %d" % Blockdata["myid"])
#if Blockdata["myid"] == Blockdata["main_node"]: print("start to compute averages")
for iavg in xrange(image_start, image_end):
mlist = data_list[iavg]
if options.noctf:
new_avg, frc, plist = compute_average_noctf(
mlist, Tracker["constants"]["radius"])
else:
new_avg, frc, plist = compute_average_ctf(
mlist, Tracker["constants"]["radius"])
FH1 = get_optimistic_res(frc)
#write_text_file(frc, os.path.join(Tracker["constants"]["masterdir"], "fsc%03d_before_ali.txt"%iavg))
if not options.skip_local_alignment:
new_average1 = within_group_refinement([mlist[kik] for kik in xrange(0,len(mlist),2)], maskfile= None, randomize= False, ir=1.0, \
ou=Tracker["constants"]["radius"], rs=1.0, xrng=[x_range], yrng=[y_range], step=[Tracker["constants"]["xstep"]], \
dst=0.0, maxit=Tracker["constants"]["maxit"], FH=max(Tracker["constants"]["FH"], FH1), FF=0.1)
new_average2 = within_group_refinement([mlist[kik] for kik in xrange(1,len(mlist),2)], maskfile= None, randomize= False, ir=1.0, \
ou= Tracker["constants"]["radius"], rs=1.0, xrng=[ x_range], yrng=[y_range], step=[Tracker["constants"]["xstep"]], \
dst=0.0, maxit=Tracker["constants"]["maxit"], FH = max(Tracker["constants"]["FH"], FH1), FF=0.1)
if options.noctf:
new_avg, frc, plist = compute_average_noctf(
mlist, Tracker["constants"]["radius"])
else:
new_avg, frc, plist = compute_average_ctf(
mlist, Tracker["constants"]["radius"])
plist_dict[iavg] = plist
FH2 = get_optimistic_res(frc)
else:
FH2 = 0.0
#write_text_file(frc, os.path.join(Tracker["constants"]["masterdir"], "fsc%03d.txt"%iavg))
FH_list[iavg] = [iavg, FH1, FH2]
if options.B_enhance:
new_avg, gb = apply_enhancement(
new_avg, Tracker["constants"]["B_start"],
Tracker["constants"]["pixel_size"],
Tracker["constants"]["Bfactor"])
print("Process avg %d %f %f %f" % (iavg, gb, FH1, FH2))
elif options.adjust_to_given_pw2:
roo = read_text_file(Tracker["constants"]["modelpw"], -1)
roo = roo[0] # always on the first column
new_avg = adjust_pw_to_model(
new_avg, Tracker["constants"]["pixel_size"], roo)
print("Process avg %d %f %f" % (iavg, FH1, FH2))
elif adjust_to_analytic_model:
new_avg = adjust_pw_to_model(
new_avg, Tracker["constants"]["pixel_size"], None)
print("Process avg %d %f %f" % (iavg, FH1, FH2))
elif options.no_adjustment:
pass
if Tracker["constants"]["low_pass_filter"] != -1.:
new_avg = filt_tanl(new_avg,
Tracker["constants"]["low_pass_filter"],
0.1)
if Tracker["constants"]["low_pass_filter"] != -1.:
if Tracker["constants"]["low_pass_filter"] == 1.:
low_pass_filter = FH1
elif Tracker["constants"]["low_pass_filter"] == 2.:
low_pass_filter = FH2
if options.skip_local_alignment: low_pass_filter = FH1
else:
low_pass_filter = Tracker["constants"]["low_pass_filter"]
if low_pass_filter >= 0.45: low_pass_filter = 0.45
new_avg = filt_tanl(new_avg, low_pass_filter, 0.1)
else:
if enforced_to_H1: new_avg = filt_tanl(new_avg, FH1, 0.1)
if options.B_enhance: new_avg = fft(new_avg)
new_avg.set_attr("members", list_dict[iavg])
new_avg.set_attr("n_objects", len(list_dict[iavg]))
slist[iavg] = new_avg
## send to main node to write
mpi_barrier(MPI_COMM_WORLD)
for im in xrange(navg):
# avg
if cpu_dict[im] == Blockdata[
"myid"] and Blockdata["myid"] != Blockdata["main_node"]:
send_EMData(slist[im], Blockdata["main_node"], tag_sharpen_avg)
elif cpu_dict[im] == Blockdata["myid"] and Blockdata[
"myid"] == Blockdata["main_node"]:
slist[im].set_attr("members", memlist[im])
slist[im].write_image(
os.path.join(Tracker["constants"]["masterdir"],
"class_averages.hdf"), im)
elif cpu_dict[im] != Blockdata["myid"] and Blockdata[
"myid"] == Blockdata["main_node"]:
new_avg_other_cpu = recv_EMData(cpu_dict[im], tag_sharpen_avg)
new_avg_other_cpu.set_attr("members", memlist[im])
new_avg_other_cpu.write_image(
os.path.join(Tracker["constants"]["masterdir"],
"class_averages.hdf"), im)
if not options.skip_local_alignment:
if cpu_dict[im] == Blockdata["myid"]:
write_text_row(
plist_dict[im],
os.path.join(Tracker["constants"]["masterdir"],
"ali2d_local_params_avg_%03d.txt" % im))
if cpu_dict[im] == Blockdata[
"myid"] and cpu_dict[im] != Blockdata["main_node"]:
wrap_mpi_send(plist_dict[im], Blockdata["main_node"],
MPI_COMM_WORLD)
wrap_mpi_send(FH_list, Blockdata["main_node"],
MPI_COMM_WORLD)
elif cpu_dict[im] != Blockdata["main_node"] and Blockdata[
"myid"] == Blockdata["main_node"]:
dummy = wrap_mpi_recv(cpu_dict[im], MPI_COMM_WORLD)
plist_dict[im] = dummy
dummy = wrap_mpi_recv(cpu_dict[im], MPI_COMM_WORLD)
FH_list[im] = dummy[im]
else:
if cpu_dict[im] == Blockdata[
"myid"] and cpu_dict[im] != Blockdata["main_node"]:
wrap_mpi_send(FH_list, Blockdata["main_node"],
MPI_COMM_WORLD)
elif cpu_dict[im] != Blockdata["main_node"] and Blockdata[
"myid"] == Blockdata["main_node"]:
dummy = wrap_mpi_recv(cpu_dict[im], MPI_COMM_WORLD)
FH_list[im] = dummy[im]
mpi_barrier(MPI_COMM_WORLD)
mpi_barrier(MPI_COMM_WORLD)
if not options.skip_local_alignment:
if Blockdata["myid"] == Blockdata["main_node"]:
ali3d_local_params = [None for im in xrange(len(ptl_list))]
for im in xrange(len(ptl_list)):
ali3d_local_params[im] = [ptl_list[im]] + plist_dict[
global_dict[ptl_list[im]][0]][global_dict[ptl_list[im]][1]]
write_text_row(
ali3d_local_params,
os.path.join(Tracker["constants"]["masterdir"],
"ali2d_local_params.txt"))
write_text_row(
FH_list,
os.path.join(Tracker["constants"]["masterdir"], "FH_list.txt"))
else:
if Blockdata["myid"] == Blockdata["main_node"]:
write_text_row(
FH_list,
os.path.join(Tracker["constants"]["masterdir"], "FH_list.txt"))
mpi_barrier(MPI_COMM_WORLD)
target_xr = 3
target_yr = 3
if (Blockdata["myid"] == 0):
cmd = "{} {} {} {} {} {} {} {} {} {}".format("sxchains.py", os.path.join(Tracker["constants"]["masterdir"],"class_averages.hdf"),\
os.path.join(Tracker["constants"]["masterdir"],"junk.hdf"),os.path.join(Tracker["constants"]["masterdir"],"ordered_class_averages.hdf"),\
"--circular","--radius=%d"%Tracker["constants"]["radius"] , "--xr=%d"%(target_xr+1),"--yr=%d"%(target_yr+1),"--align", ">/dev/null")
junk = cmdexecute(cmd)
cmd = "{} {}".format(
"rm -rf",
os.path.join(Tracker["constants"]["masterdir"], "junk.hdf"))
junk = cmdexecute(cmd)
from mpi import mpi_finalize
mpi_finalize()
exit()
def main():
def params_3D_2D_NEW(phi, theta, psi, s2x, s2y, mirror):
if mirror:
m = 1
alpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0, 540.0-psi, 0, 0, 1.0)
else:
m = 0
alpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0, 360.0-psi, 0, 0, 1.0)
return alpha, sx, sy, m
progname = os.path.basename(sys.argv[0])
usage = progname + " prj_stack --ave2D= --var2D= --ave3D= --var3D= --img_per_grp= --fl=0.2 --aa=0.1 --sym=symmetry --CTF"
parser = OptionParser(usage, version=SPARXVERSION)
parser.add_option("--ave2D", type="string" , default=False, help="write to the disk a stack of 2D averages")
parser.add_option("--var2D", type="string" , default=False, help="write to the disk a stack of 2D variances")
parser.add_option("--ave3D", type="string" , default=False, help="write to the disk reconstructed 3D average")
parser.add_option("--var3D", type="string" , default=False, help="compute 3D variability (time consuming!)")
parser.add_option("--img_per_grp", type="int" , default=10 , help="number of neighbouring projections")
parser.add_option("--no_norm", action="store_true", default=False, help="do not use normalization")
parser.add_option("--radiusvar", type="int" , default=-1 , help="radius for 3D var" )
parser.add_option("--npad", type="int" , default=2 , help="number of time to pad the original images")
parser.add_option("--sym" , type="string" , default="c1" , help="symmetry")
parser.add_option("--fl", type="float" , default=0.0 , help="stop-band frequency (Default - no filtration)")
parser.add_option("--aa", type="float" , default=0.0 , help="fall off of the filter (Default - no filtration)")
parser.add_option("--CTF", action="store_true", default=False, help="use CFT correction")
parser.add_option("--VERBOSE", action="store_true", default=False, help="Long output for debugging")
#parser.add_option("--MPI" , action="store_true", default=False, help="use MPI version")
#parser.add_option("--radiuspca", type="int" , default=-1 , help="radius for PCA" )
#parser.add_option("--iter", type="int" , default=40 , help="maximum number of iterations (stop criterion of reconstruction process)" )
#parser.add_option("--abs", type="float" , default=0.0 , help="minimum average absolute change of voxels' values (stop criterion of reconstruction process)" )
#parser.add_option("--squ", type="float" , default=0.0 , help="minimum average squared change of voxels' values (stop criterion of reconstruction process)" )
parser.add_option("--VAR" , action="store_true", default=False, help="stack on input consists of 2D variances (Default False)")
parser.add_option("--decimate", type="float", default=1.0, help="image decimate rate, a number large than 1. default is 1")
parser.add_option("--window", type="int", default=0, help="reduce images to a small image size without changing pixel_size. Default value is zero.")
#parser.add_option("--SND", action="store_true", default=False, help="compute squared normalized differences (Default False)")
parser.add_option("--nvec", type="int" , default=0 , help="number of eigenvectors, default = 0 meaning no PCA calculated")
parser.add_option("--symmetrize", action="store_true", default=False, help="Prepare input stack for handling symmetry (Default False)")
(options,args) = parser.parse_args()
#####
from mpi import mpi_init, mpi_comm_rank, mpi_comm_size, mpi_recv, MPI_COMM_WORLD, MPI_TAG_UB
from mpi import mpi_barrier, mpi_reduce, mpi_bcast, mpi_send, MPI_FLOAT, MPI_SUM, MPI_INT, MPI_MAX
from applications import MPI_start_end
from reconstruction import recons3d_em, recons3d_em_MPI
from reconstruction import recons3d_4nn_MPI, recons3d_4nn_ctf_MPI
from utilities import print_begin_msg, print_end_msg, print_msg
from utilities import read_text_row, get_image, get_im
from utilities import bcast_EMData_to_all, bcast_number_to_all
from utilities import get_symt
# This is code for handling symmetries by the above program. To be incorporated. PAP 01/27/2015
from EMAN2db import db_open_dict
if options.symmetrize :
try:
sys.argv = mpi_init(len(sys.argv), sys.argv)
try:
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
if( number_of_proc > 1 ):
ERROR("Cannot use more than one CPU for symmetry prepration","sx3dvariability",1)
except:
pass
except:
pass
# Input
#instack = "Clean_NORM_CTF_start_wparams.hdf"
#instack = "bdb:data"
instack = args[0]
sym = options.sym
if( sym == "c1" ):
ERROR("Thre is no need to symmetrize stack for C1 symmetry","sx3dvariability",1)
if(instack[:4] !="bdb:"):
stack = "bdb:data"
delete_bdb(stack)
cmdexecute("sxcpy.py "+instack+" "+stack)
else:
stack = instack
qt = EMUtil.get_all_attributes(stack,'xform.projection')
na = len(qt)
ts = get_symt(sym)
ks = len(ts)
angsa = [None]*na
for k in xrange(ks):
delete_bdb("bdb:Q%1d"%k)
cmdexecute("e2bdb.py "+stack+" --makevstack=bdb:Q%1d"%k)
DB = db_open_dict("bdb:Q%1d"%k)
for i in xrange(na):
ut = qt[i]*ts[k]
DB.set_attr(i, "xform.projection", ut)
#bt = ut.get_params("spider")
#angsa[i] = [round(bt["phi"],3)%360.0, round(bt["theta"],3)%360.0, bt["psi"], -bt["tx"], -bt["ty"]]
#write_text_row(angsa, 'ptsma%1d.txt'%k)
#cmdexecute("e2bdb.py "+stack+" --makevstack=bdb:Q%1d"%k)
#cmdexecute("sxheader.py bdb:Q%1d --params=xform.projection --import=ptsma%1d.txt"%(k,k))
DB.close()
delete_bdb("bdb:sdata")
cmdexecute("e2bdb.py . --makevstack=bdb:sdata --filt=Q")
#cmdexecute("ls EMAN2DB/sdata*")
a = get_im("bdb:sdata")
a.set_attr("variabilitysymmetry",sym)
a.write_image("bdb:sdata")
else:
sys.argv = mpi_init(len(sys.argv), sys.argv)
myid = mpi_comm_rank(MPI_COMM_WORLD)
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
main_node = 0
if len(args) == 1:
stack = args[0]
else:
print( "usage: " + usage)
print( "Please run '" + progname + " -h' for detailed options")
return 1
t0 = time()
# obsolete flags
options.MPI = True
options.nvec = 0
options.radiuspca = -1
options.iter = 40
options.abs = 0.0
options.squ = 0.0
if options.fl > 0.0 and options.aa == 0.0:
ERROR("Fall off has to be given for the low-pass filter", "sx3dvariability", 1, myid)
if options.VAR and options.SND:
ERROR("Only one of var and SND can be set!", "sx3dvariability", myid)
exit()
if options.VAR and (options.ave2D or options.ave3D or options.var2D):
ERROR("When VAR is set, the program cannot output ave2D, ave3D or var2D", "sx3dvariability", 1, myid)
exit()
#if options.SND and (options.ave2D or options.ave3D):
# ERROR("When SND is set, the program cannot output ave2D or ave3D", "sx3dvariability", 1, myid)
# exit()
if options.nvec > 0 :
ERROR("PCA option not implemented", "sx3dvariability", 1, myid)
exit()
if options.nvec > 0 and options.ave3D == None:
ERROR("When doing PCA analysis, one must set ave3D", "sx3dvariability", myid=myid)
exit()
import string
options.sym = options.sym.lower()
if global_def.CACHE_DISABLE:
from utilities import disable_bdb_cache
disable_bdb_cache()
global_def.BATCH = True
if myid == main_node:
print_begin_msg("sx3dvariability")
print_msg("%-70s: %s\n"%("Input stack", stack))
img_per_grp = options.img_per_grp
nvec = options.nvec
radiuspca = options.radiuspca
symbaselen = 0
if myid == main_node:
nima = EMUtil.get_image_count(stack)
img = get_image(stack)
nx = img.get_xsize()
ny = img.get_ysize()
if options.sym != "c1" :
imgdata = get_im(stack)
try:
i = imgdata.get_attr("variabilitysymmetry")
if(i != options.sym):
ERROR("The symmetry provided does not agree with the symmetry of the input stack", "sx3dvariability", myid=myid)
except:
ERROR("Input stack is not prepared for symmetry, please follow instructions", "sx3dvariability", myid=myid)
from utilities import get_symt
i = len(get_symt(options.sym))
if((nima/i)*i != nima):
ERROR("The length of the input stack is incorrect for symmetry processing", "sx3dvariability", myid=myid)
symbaselen = nima/i
else: symbaselen = nima
else:
nima = 0
nx = 0
ny = 0
nima = bcast_number_to_all(nima)
nx = bcast_number_to_all(nx)
ny = bcast_number_to_all(ny)
Tracker ={}
Tracker["nx"] =nx
Tracker["ny"] =ny
Tracker["total_stack"]=nima
if options.decimate==1.:
if options.window !=0:
nx = options.window
ny = options.window
else:
if options.window ==0:
nx = int(nx/options.decimate)
ny = int(ny/options.decimate)
else:
nx = int(options.window/options.decimate)
ny = nx
symbaselen = bcast_number_to_all(symbaselen)
if radiuspca == -1: radiuspca = nx/2-2
if myid == main_node:
print_msg("%-70s: %d\n"%("Number of projection", nima))
img_begin, img_end = MPI_start_end(nima, number_of_proc, myid)
"""
if options.SND:
from projection import prep_vol, prgs
from statistics import im_diff
from utilities import get_im, model_circle, get_params_proj, set_params_proj
from utilities import get_ctf, generate_ctf
from filter import filt_ctf
imgdata = EMData.read_images(stack, range(img_begin, img_end))
if options.CTF:
vol = recons3d_4nn_ctf_MPI(myid, imgdata, 1.0, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
else:
vol = recons3d_4nn_MPI(myid, imgdata, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
bcast_EMData_to_all(vol, myid)
volft, kb = prep_vol(vol)
mask = model_circle(nx/2-2, nx, ny)
varList = []
for i in xrange(img_begin, img_end):
phi, theta, psi, s2x, s2y = get_params_proj(imgdata[i-img_begin])
ref_prj = prgs(volft, kb, [phi, theta, psi, -s2x, -s2y])
if options.CTF:
ctf_params = get_ctf(imgdata[i-img_begin])
ref_prj = filt_ctf(ref_prj, generate_ctf(ctf_params))
diff, A, B = im_diff(ref_prj, imgdata[i-img_begin], mask)
diff2 = diff*diff
set_params_proj(diff2, [phi, theta, psi, s2x, s2y])
varList.append(diff2)
mpi_barrier(MPI_COMM_WORLD)
"""
if options.VAR:
#varList = EMData.read_images(stack, range(img_begin, img_end))
varList = []
this_image = EMData()
for index_of_particle in xrange(img_begin,img_end):
this_image.read_image(stack,index_of_particle)
varList.append(image_decimate_window_xform_ctf(img,options.decimate,options.window,options.CTF))
else:
from utilities import bcast_number_to_all, bcast_list_to_all, send_EMData, recv_EMData
from utilities import set_params_proj, get_params_proj, params_3D_2D, get_params2D, set_params2D, compose_transform2
from utilities import model_blank, nearest_proj, model_circle
from applications import pca
from statistics import avgvar, avgvar_ctf, ccc
from filter import filt_tanl
from morphology import threshold, square_root
from projection import project, prep_vol, prgs
from sets import Set
if myid == main_node:
t1 = time()
proj_angles = []
aveList = []
tab = EMUtil.get_all_attributes(stack, 'xform.projection')
for i in xrange(nima):
t = tab[i].get_params('spider')
phi = t['phi']
theta = t['theta']
psi = t['psi']
x = theta
if x > 90.0: x = 180.0 - x
x = x*10000+psi
proj_angles.append([x, t['phi'], t['theta'], t['psi'], i])
t2 = time()
print_msg("%-70s: %d\n"%("Number of neighboring projections", img_per_grp))
print_msg("...... Finding neighboring projections\n")
if options.VERBOSE:
print "Number of images per group: ", img_per_grp
print "Now grouping projections"
proj_angles.sort()
proj_angles_list = [0.0]*(nima*4)
if myid == main_node:
for i in xrange(nima):
proj_angles_list[i*4] = proj_angles[i][1]
proj_angles_list[i*4+1] = proj_angles[i][2]
proj_angles_list[i*4+2] = proj_angles[i][3]
proj_angles_list[i*4+3] = proj_angles[i][4]
proj_angles_list = bcast_list_to_all(proj_angles_list, myid, main_node)
proj_angles = []
for i in xrange(nima):
proj_angles.append([proj_angles_list[i*4], proj_angles_list[i*4+1], proj_angles_list[i*4+2], int(proj_angles_list[i*4+3])])
del proj_angles_list
proj_list, mirror_list = nearest_proj(proj_angles, img_per_grp, range(img_begin, img_end))
all_proj = Set()
for im in proj_list:
for jm in im:
all_proj.add(proj_angles[jm][3])
all_proj = list(all_proj)
if options.VERBOSE:
print "On node %2d, number of images needed to be read = %5d"%(myid, len(all_proj))
index = {}
for i in xrange(len(all_proj)): index[all_proj[i]] = i
mpi_barrier(MPI_COMM_WORLD)
if myid == main_node:
print_msg("%-70s: %.2f\n"%("Finding neighboring projections lasted [s]", time()-t2))
print_msg("%-70s: %d\n"%("Number of groups processed on the main node", len(proj_list)))
if options.VERBOSE:
print "Grouping projections took: ", (time()-t2)/60 , "[min]"
print "Number of groups on main node: ", len(proj_list)
mpi_barrier(MPI_COMM_WORLD)
if myid == main_node:
print_msg("...... calculating the stack of 2D variances \n")
if options.VERBOSE:
print "Now calculating the stack of 2D variances"
proj_params = [0.0]*(nima*5)
aveList = []
varList = []
if nvec > 0:
eigList = [[] for i in xrange(nvec)]
if options.VERBOSE: print "Begin to read images on processor %d"%(myid)
ttt = time()
#imgdata = EMData.read_images(stack, all_proj)
img = EMData()
imgdata = []
for index_of_proj in xrange(len(all_proj)):
img.read_image(stack, all_proj[index_of_proj])
dmg = image_decimate_window_xform_ctf(img,options.decimate,options.window,options.CTF)
#print dmg.get_xsize(), "init"
imgdata.append(dmg)
if options.VERBOSE:
print "Reading images on processor %d done, time = %.2f"%(myid, time()-ttt)
print "On processor %d, we got %d images"%(myid, len(imgdata))
mpi_barrier(MPI_COMM_WORLD)
'''
imgdata2 = EMData.read_images(stack, range(img_begin, img_end))
if options.fl > 0.0:
for k in xrange(len(imgdata2)):
imgdata2[k] = filt_tanl(imgdata2[k], options.fl, options.aa)
if options.CTF:
vol = recons3d_4nn_ctf_MPI(myid, imgdata2, 1.0, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
else:
vol = recons3d_4nn_MPI(myid, imgdata2, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
if myid == main_node:
vol.write_image("vol_ctf.hdf")
print_msg("Writing to the disk volume reconstructed from averages as : %s\n"%("vol_ctf.hdf"))
del vol, imgdata2
mpi_barrier(MPI_COMM_WORLD)
'''
from applications import prepare_2d_forPCA
from utilities import model_blank
for i in xrange(len(proj_list)):
ki = proj_angles[proj_list[i][0]][3]
if ki >= symbaselen: continue
mi = index[ki]
phiM, thetaM, psiM, s2xM, s2yM = get_params_proj(imgdata[mi])
grp_imgdata = []
for j in xrange(img_per_grp):
mj = index[proj_angles[proj_list[i][j]][3]]
phi, theta, psi, s2x, s2y = get_params_proj(imgdata[mj])
alpha, sx, sy, mirror = params_3D_2D_NEW(phi, theta, psi, s2x, s2y, mirror_list[i][j])
if thetaM <= 90:
if mirror == 0: alpha, sx, sy, scale = compose_transform2(alpha, sx, sy, 1.0, phiM-phi, 0.0, 0.0, 1.0)
else: alpha, sx, sy, scale = compose_transform2(alpha, sx, sy, 1.0, 180-(phiM-phi), 0.0, 0.0, 1.0)
else:
if mirror == 0: alpha, sx, sy, scale = compose_transform2(alpha, sx, sy, 1.0, -(phiM-phi), 0.0, 0.0, 1.0)
else: alpha, sx, sy, scale = compose_transform2(alpha, sx, sy, 1.0, -(180-(phiM-phi)), 0.0, 0.0, 1.0)
set_params2D(imgdata[mj], [alpha, sx, sy, mirror, 1.0])
grp_imgdata.append(imgdata[mj])
#print grp_imgdata[j].get_xsize(), imgdata[mj].get_xsize()
if not options.no_norm:
#print grp_imgdata[j].get_xsize()
mask = model_circle(nx/2-2, nx, nx)
for k in xrange(img_per_grp):
ave, std, minn, maxx = Util.infomask(grp_imgdata[k], mask, False)
grp_imgdata[k] -= ave
grp_imgdata[k] /= std
del mask
if options.fl > 0.0:
from filter import filt_ctf, filt_table
from fundamentals import fft, window2d
nx2 = 2*nx
ny2 = 2*ny
if options.CTF:
from utilities import pad
for k in xrange(img_per_grp):
grp_imgdata[k] = window2d(fft( filt_tanl( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1), options.fl, options.aa) ),nx,ny)
#grp_imgdata[k] = window2d(fft( filt_table( filt_tanl( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1), options.fl, options.aa), fifi) ),nx,ny)
#grp_imgdata[k] = filt_tanl(grp_imgdata[k], options.fl, options.aa)
else:
for k in xrange(img_per_grp):
grp_imgdata[k] = filt_tanl( grp_imgdata[k], options.fl, options.aa)
#grp_imgdata[k] = window2d(fft( filt_table( filt_tanl( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1), options.fl, options.aa), fifi) ),nx,ny)
#grp_imgdata[k] = filt_tanl(grp_imgdata[k], options.fl, options.aa)
else:
from utilities import pad, read_text_file
from filter import filt_ctf, filt_table
from fundamentals import fft, window2d
nx2 = 2*nx
ny2 = 2*ny
if options.CTF:
from utilities import pad
for k in xrange(img_per_grp):
grp_imgdata[k] = window2d( fft( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1) ) , nx,ny)
#grp_imgdata[k] = window2d(fft( filt_table( filt_tanl( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1), options.fl, options.aa), fifi) ),nx,ny)
#grp_imgdata[k] = filt_tanl(grp_imgdata[k], options.fl, options.aa)
'''
if i < 10 and myid == main_node:
for k in xrange(10):
grp_imgdata[k].write_image("grp%03d.hdf"%i, k)
'''
"""
if myid == main_node and i==0:
for pp in xrange(len(grp_imgdata)):
grp_imgdata[pp].write_image("pp.hdf", pp)
"""
ave, grp_imgdata = prepare_2d_forPCA(grp_imgdata)
"""
if myid == main_node and i==0:
for pp in xrange(len(grp_imgdata)):
grp_imgdata[pp].write_image("qq.hdf", pp)
"""
var = model_blank(nx,ny)
for q in grp_imgdata: Util.add_img2( var, q )
Util.mul_scalar( var, 1.0/(len(grp_imgdata)-1))
# Switch to std dev
var = square_root(threshold(var))
#if options.CTF: ave, var = avgvar_ctf(grp_imgdata, mode="a")
#else: ave, var = avgvar(grp_imgdata, mode="a")
"""
if myid == main_node:
ave.write_image("avgv.hdf",i)
var.write_image("varv.hdf",i)
"""
set_params_proj(ave, [phiM, thetaM, 0.0, 0.0, 0.0])
set_params_proj(var, [phiM, thetaM, 0.0, 0.0, 0.0])
aveList.append(ave)
varList.append(var)
if options.VERBOSE:
print "%5.2f%% done on processor %d"%(i*100.0/len(proj_list), myid)
if nvec > 0:
eig = pca(input_stacks=grp_imgdata, subavg="", mask_radius=radiuspca, nvec=nvec, incore=True, shuffle=False, genbuf=True)
for k in xrange(nvec):
set_params_proj(eig[k], [phiM, thetaM, 0.0, 0.0, 0.0])
eigList[k].append(eig[k])
"""
if myid == 0 and i == 0:
for k in xrange(nvec):
eig[k].write_image("eig.hdf", k)
"""
del imgdata
# To this point, all averages, variances, and eigenvectors are computed
if options.ave2D:
from fundamentals import fpol
if myid == main_node:
km = 0
for i in xrange(number_of_proc):
if i == main_node :
for im in xrange(len(aveList)):
aveList[im].write_image(options.ave2D, km)
km += 1
else:
nl = mpi_recv(1, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
nl = int(nl[0])
for im in xrange(nl):
ave = recv_EMData(i, im+i+70000)
"""
nm = mpi_recv(1, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
nm = int(nm[0])
members = mpi_recv(nm, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
ave.set_attr('members', map(int, members))
members = mpi_recv(nm, MPI_FLOAT, i, MPI_TAG_UB, MPI_COMM_WORLD)
ave.set_attr('pix_err', map(float, members))
members = mpi_recv(3, MPI_FLOAT, i, MPI_TAG_UB, MPI_COMM_WORLD)
ave.set_attr('refprojdir', map(float, members))
"""
tmpvol=fpol(ave, Tracker["nx"],Tracker["nx"],Tracker["nx"])
tmpvol.write_image(options.ave2D, km)
km += 1
else:
mpi_send(len(aveList), 1, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
for im in xrange(len(aveList)):
send_EMData(aveList[im], main_node,im+myid+70000)
"""
members = aveList[im].get_attr('members')
mpi_send(len(members), 1, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
mpi_send(members, len(members), MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
members = aveList[im].get_attr('pix_err')
mpi_send(members, len(members), MPI_FLOAT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
try:
members = aveList[im].get_attr('refprojdir')
mpi_send(members, 3, MPI_FLOAT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
except:
mpi_send([-999.0,-999.0,-999.0], 3, MPI_FLOAT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
"""
if options.ave3D:
from fundamentals import fpol
if options.VERBOSE:
print "Reconstructing 3D average volume"
ave3D = recons3d_4nn_MPI(myid, aveList, symmetry=options.sym, npad=options.npad)
bcast_EMData_to_all(ave3D, myid)
if myid == main_node:
ave3D=fpol(ave3D,Tracker["nx"],Tracker["nx"],Tracker["nx"])
ave3D.write_image(options.ave3D)
print_msg("%-70s: %s\n"%("Writing to the disk volume reconstructed from averages as", options.ave3D))
del ave, var, proj_list, stack, phi, theta, psi, s2x, s2y, alpha, sx, sy, mirror, aveList
if nvec > 0:
for k in xrange(nvec):
if options.VERBOSE:
print "Reconstruction eigenvolumes", k
cont = True
ITER = 0
mask2d = model_circle(radiuspca, nx, nx)
while cont:
#print "On node %d, iteration %d"%(myid, ITER)
eig3D = recons3d_4nn_MPI(myid, eigList[k], symmetry=options.sym, npad=options.npad)
bcast_EMData_to_all(eig3D, myid, main_node)
if options.fl > 0.0:
eig3D = filt_tanl(eig3D, options.fl, options.aa)
if myid == main_node:
eig3D.write_image("eig3d_%03d.hdf"%k, ITER)
Util.mul_img( eig3D, model_circle(radiuspca, nx, nx, nx) )
eig3Df, kb = prep_vol(eig3D)
del eig3D
cont = False
icont = 0
for l in xrange(len(eigList[k])):
phi, theta, psi, s2x, s2y = get_params_proj(eigList[k][l])
proj = prgs(eig3Df, kb, [phi, theta, psi, s2x, s2y])
cl = ccc(proj, eigList[k][l], mask2d)
if cl < 0.0:
icont += 1
cont = True
eigList[k][l] *= -1.0
u = int(cont)
u = mpi_reduce([u], 1, MPI_INT, MPI_MAX, main_node, MPI_COMM_WORLD)
icont = mpi_reduce([icont], 1, MPI_INT, MPI_SUM, main_node, MPI_COMM_WORLD)
if myid == main_node:
u = int(u[0])
print " Eigenvector: ",k," number changed ",int(icont[0])
else: u = 0
u = bcast_number_to_all(u, main_node)
cont = bool(u)
ITER += 1
del eig3Df, kb
mpi_barrier(MPI_COMM_WORLD)
del eigList, mask2d
if options.ave3D: del ave3D
if options.var2D:
from fundamentals import fpol
if myid == main_node:
km = 0
for i in xrange(number_of_proc):
if i == main_node :
for im in xrange(len(varList)):
tmpvol=fpol(varList[im], Tracker["nx"], Tracker["nx"],1)
tmpvol.write_image(options.var2D, km)
km += 1
else:
nl = mpi_recv(1, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
nl = int(nl[0])
for im in xrange(nl):
ave = recv_EMData(i, im+i+70000)
tmpvol=fpol(ave, Tracker["nx"], Tracker["nx"],1)
tmpvol.write_image(options.var2D, km)
km += 1
else:
mpi_send(len(varList), 1, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
for im in xrange(len(varList)):
send_EMData(varList[im], main_node, im+myid+70000)# What with the attributes??
mpi_barrier(MPI_COMM_WORLD)
if options.var3D:
if myid == main_node and options.VERBOSE:
print "Reconstructing 3D variability volume"
t6 = time()
radiusvar = options.radiusvar
if( radiusvar < 0 ): radiusvar = nx//2 -3
res = recons3d_4nn_MPI(myid, varList, symmetry=options.sym, npad=options.npad)
#res = recons3d_em_MPI(varList, vol_stack, options.iter, radiusvar, options.abs, True, options.sym, options.squ)
if myid == main_node:
from fundamentals import fpol
res =fpol(res, Tracker["nx"], Tracker["nx"], Tracker["nx"])
res.write_image(options.var3D)
if myid == main_node:
print_msg("%-70s: %.2f\n"%("Reconstructing 3D variability took [s]", time()-t6))
if options.VERBOSE:
print "Reconstruction took: %.2f [min]"%((time()-t6)/60)
if myid == main_node:
print_msg("%-70s: %.2f\n"%("Total time for these computations [s]", time()-t0))
if options.VERBOSE:
print "Total time for these computations: %.2f [min]"%((time()-t0)/60)
print_end_msg("sx3dvariability")
global_def.BATCH = False
from mpi import mpi_finalize
mpi_finalize()
def filterlocal(ui, vi, m, falloff, myid, main_node, number_of_proc): from mpi import mpi_init, mpi_comm_size, mpi_comm_rank, MPI_COMM_WORLD from mpi import mpi_reduce, mpi_bcast, mpi_barrier, mpi_gatherv, mpi_send, mpi_recv from mpi import MPI_SUM, MPI_FLOAT, MPI_INT from utilities import bcast_number_to_all, bcast_list_to_all, model_blank, bcast_EMData_to_all, reduce_EMData_to_root from morphology import threshold_outside from filter import filt_tanl from fundamentals import fft, fftip if(myid == main_node): nx = vi.get_xsize() ny = vi.get_ysize() nz = vi.get_zsize() # Round all resolution numbers to two digits for x in xrange(nx): for y in xrange(ny): for z in xrange(nz): ui.set_value_at_fast( x,y,z, round(ui.get_value_at(x,y,z), 2) ) dis = [nx,ny,nz] else: falloff = 0.0 radius = 0 dis = [0,0,0] falloff = bcast_number_to_all(falloff, main_node) dis = bcast_list_to_all(dis, myid, source_node = main_node) if(myid != main_node): nx = int(dis[0]) ny = int(dis[1]) nz = int(dis[2]) vi = model_blank(nx,ny,nz) ui = model_blank(nx,ny,nz) bcast_EMData_to_all(vi, myid, main_node) bcast_EMData_to_all(ui, myid, main_node) fftip(vi) # volume to be filtered st = Util.infomask(ui, m, True) filteredvol = model_blank(nx,ny,nz) cutoff = max(st[2] - 0.01,0.0) while(cutoff < st[3] ): cutoff = round(cutoff + 0.01, 2) #if(myid == main_node): print cutoff,st pt = Util.infomask( threshold_outside(ui, cutoff - 0.00501, cutoff + 0.005), m, True) # Ideally, one would want to check only slices in question... if(pt[0] != 0.0): #print cutoff,pt[0] vovo = fft( filt_tanl(vi, cutoff, falloff) ) for z in xrange(myid, nz, number_of_proc): for x in xrange(nx): for y in xrange(ny): if(m.get_value_at(x,y,z) > 0.5): if(round(ui.get_value_at(x,y,z),2) == cutoff): filteredvol.set_value_at_fast(x,y,z,vovo.get_value_at(x,y,z)) mpi_barrier(MPI_COMM_WORLD) reduce_EMData_to_root(filteredvol, myid, main_node, MPI_COMM_WORLD) return filteredvol
def dovolume(ref_data):
from utilities import print_msg, read_text_row
from filter import fit_tanh, filt_tanl
from fundamentals import fshift
from morphology import threshold
# Prepare the reference in 3D alignment, this function corresponds to what do_volume does.
# Input: list ref_data
# 0 - mask
# 1 - center flag
# 2 - raw average
# 3 - fsc result
# Output: filtered, centered, and masked reference image
# apply filtration (FSC) to reference image:
global ref_ali2d_counter
ref_ali2d_counter += 1
fl = ref_data[2].cmp("dot", ref_data[2], {
"negative": 0,
"mask": ref_data[0]
})
print_msg("do_volume user function Step = %5d GOAL = %10.3e\n" %
(ref_ali2d_counter, fl))
stat = Util.infomask(ref_data[2], ref_data[0], False)
vol = ref_data[2] - stat[0]
Util.mul_scalar(vol, 1.0 / stat[1])
vol = threshold(vol)
#Util.mul_img(vol, ref_data[0])
try:
aa = read_text_row("flaa.txt")[0]
fl = aa[0]
aa = aa[1]
except:
fl = 0.4
aa = 0.2
msg = "Tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n" % (
fl, aa)
print_msg(msg)
from utilities import read_text_file
from fundamentals import rops_table, fftip, fft
from filter import filt_table, filt_btwl
fftip(vol)
try:
rt = read_text_file("pwreference.txt")
ro = rops_table(vol)
# Here unless I am mistaken it is enough to take the beginning of the reference pw.
for i in xrange(1, len(ro)):
ro[i] = (rt[i] / ro[i])**0.5
vol = fft(filt_table(filt_tanl(vol, fl, aa), ro))
msg = "Power spectrum adjusted\n"
print_msg(msg)
except:
vol = fft(filt_tanl(vol, fl, aa))
stat = Util.infomask(vol, ref_data[0], False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0 / stat[1])
vol = threshold(vol)
vol = filt_btwl(vol, 0.38, 0.5)
Util.mul_img(vol, ref_data[0])
if ref_data[1] == 1:
cs = volf.phase_cog()
msg = "Center x = %10.3f Center y = %10.3f Center z = %10.3f\n" % (
cs[0], cs[1], cs[2])
print_msg(msg)
volf = fshift(volf, -cs[0], -cs[1], -cs[2])
else:
cs = [0.0] * 3
return vol, cs
def runcheck(classavgstack, reconfile, outdir, inangles=None, selectdoc=None, prjmethod='trilinear', displayYN=False,
projstack='proj.hdf', outangles='angles.txt', outstack='comp-proj-reproj.hdf', normstack='comp-proj-reproj-norm.hdf'):
print("\n%s, Modified 2018-12-07\n" % __file__)
# Check if inputs exist
check(classavgstack)
check(reconfile)
# Create directory if it doesn't exist
if not os.path.isdir(outdir):
os.makedirs(outdir) # os.mkdir() can only operate one directory deep
print("mkdir -p %s" % outdir)
# Expand path for outputs
projstack = os.path.join(outdir, projstack)
outangles = os.path.join(outdir, outangles)
outstack = os.path.join(outdir, outstack)
normstack = os.path.join(outdir, normstack)
# Get number of images
nimg0 = EMAN2_cppwrap.EMUtil.get_image_count(classavgstack)
recon = EMAN2_cppwrap.EMData(reconfile)
nx = recon.get_xsize()
# In case class averages include discarded images, apply selection file
if selectdoc:
goodavgs, extension = os.path.splitext(classavgstack)
newclasses = goodavgs + "_kept" + extension
# e2proc2d appends to existing files, so rename existing output
if os.path.exists(newclasses):
renamefile = newclasses + '.bak'
os.rename(newclasses, renamefile)
print("mv %s %s" % (newclasses, renamefile))
cmd7="e2proc2d.py %s %s --list=%s" % (classavgstack, newclasses, selectdoc)
print(cmd7)
os.system(cmd7)
# Update class-averages
classavgstack = newclasses
# Import Euler angles
if inangles:
cmd6 = "sxheader.py %s --params=xform.projection --import=%s" % (classavgstack, inangles)
print(cmd6)
header(classavgstack, 'xform.projection', fimport=inangles)
try:
header(classavgstack, 'xform.projection', fexport=outangles)
cmd1 = "sxheader.py %s --params=xform.projection --export=%s" % (classavgstack, outangles)
print(cmd1)
except RuntimeError:
print("\nERROR!! No projection angles found in class-average stack header!\n")
print('Usage:', USAGE)
exit()
#cmd2="sxproject3d.py %s %s --angles=%s" % (recon, projstack, outangles)
#print(cmd2)
#os.system(cmd2)
# Here if you want to be fancy, there should be an option to chose the projection method,
# the mechanism can be copied from sxproject3d.py PAP
if prjmethod=='trilinear':
method_num = 1
elif prjmethod=='gridding':
method_num = -1
elif prjmethod=='nn':
method_num = 0
else:
print("\nERROR!! Valid projection methods are: trilinear (default), gridding, and nn (nearest neighbor).")
print('Usage:', USAGE)
exit()
#project3d(recon, stack=projstack, listagls=outangles)
recon = prep_vol(recon, npad = 2, interpolation_method = 1)
result=[]
# Here you need actual radius to compute proper ccc's, but if you do, you have to deal with translations, PAP
mask = model_circle(nx//2-2,nx,nx)
# Number of images may have changed
nimg1 = EMAN2_cppwrap.EMUtil.get_image_count(classavgstack)
outangles = read_text_row(outangles)
for imgnum in range(nimg1):
# get class average
classimg = get_im(classavgstack, imgnum)
# compute re-projection
prjimg = prgl(recon, outangles[imgnum], 1, False)
# calculate 1D power spectra
rops_dst = rops_table(classimg*mask)
rops_src = rops_table(prjimg)
# Set power spectrum of reprojection to the data.
# Since data has an envelope, it would make more sense to set data to reconstruction,
# but to do it one would have to know the actual resolution of the data.
# you can check sxprocess.py --adjpw to see how this is done properly PAP
table = [0.0]*len(rops_dst) # initialize table
for j in range( len(rops_dst) ):
table[j] = sqrt( old_div(rops_dst[j],rops_src[j]) )
prjimg = fft(filt_table(prjimg, table)) # match FFT amplitdes of re-projection and class average
cccoeff = ccc(prjimg, classimg, mask)
#print(imgnum, cccoeff)
classimg.set_attr_dict({'cross-corr':cccoeff})
prjimg.set_attr_dict({'cross-corr':cccoeff})
prjimg.write_image(outstack,2*imgnum)
classimg.write_image(outstack, 2*imgnum+1)
result.append(cccoeff)
del outangles
meanccc = old_div(sum(result),nimg1)
print("Average CCC is %s" % meanccc)
nimg2 = EMAN2_cppwrap.EMUtil.get_image_count(outstack)
for imgnum in xrange(nimg2):
if (imgnum % 2 ==0):
prjimg = get_im(outstack,imgnum)
meanccc1 = prjimg.get_attr_default('mean-cross-corr', -1.0)
prjimg.set_attr_dict({'mean-cross-corr':meanccc})
write_header(outstack,prjimg,imgnum)
if (imgnum % 100) == 0:
print(imgnum)
# e2proc2d appends to existing files, so delete existing output
if os.path.exists(normstack):
os.remove(normstack)
print("rm %s" % normstack)
# Why would you want to do it? If you do, it should have been done during ccc calculations,
# otherwise what is see is not corresponding to actual data, thus misleading. PAP
#cmd5="e2proc2d.py %s %s --process=normalize" % (outstack, normstack)
#print(cmd5)
#os.system(cmd5)
# Optionally pop up e2display
if displayYN:
cmd8 = "e2display.py %s" % outstack
print(cmd8)
os.system(cmd8)
print("Done!")
def main():
def params_3D_2D_NEW(phi, theta, psi, s2x, s2y, mirror):
if mirror:
m = 1
alpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0,
540.0 - psi, 0, 0, 1.0)
else:
m = 0
alpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0,
360.0 - psi, 0, 0, 1.0)
return alpha, sx, sy, m
progname = os.path.basename(sys.argv[0])
usage = progname + " prj_stack --ave2D= --var2D= --ave3D= --var3D= --img_per_grp= --fl=15. --aa=0.01 --sym=symmetry --CTF"
parser = OptionParser(usage, version=SPARXVERSION)
parser.add_option("--output_dir",
type="string",
default="./",
help="output directory")
parser.add_option("--ave2D",
type="string",
default=False,
help="write to the disk a stack of 2D averages")
parser.add_option("--var2D",
type="string",
default=False,
help="write to the disk a stack of 2D variances")
parser.add_option("--ave3D",
type="string",
default=False,
help="write to the disk reconstructed 3D average")
parser.add_option("--var3D",
type="string",
default=False,
help="compute 3D variability (time consuming!)")
parser.add_option("--img_per_grp",
type="int",
default=10,
help="number of neighbouring projections")
parser.add_option("--no_norm",
action="store_true",
default=False,
help="do not use normalization")
#parser.add_option("--radius", type="int" , default=-1 , help="radius for 3D variability" )
parser.add_option("--npad",
type="int",
default=2,
help="number of time to pad the original images")
parser.add_option("--sym", type="string", default="c1", help="symmetry")
parser.add_option(
"--fl",
type="float",
default=0.0,
help=
"cutoff freqency in absolute frequency (0.0-0.5). (Default - no filtration)"
)
parser.add_option(
"--aa",
type="float",
default=0.0,
help=
"fall off of the filter. Put 0.01 if user has no clue about falloff (Default - no filtration)"
)
parser.add_option("--CTF",
action="store_true",
default=False,
help="use CFT correction")
parser.add_option("--VERBOSE",
action="store_true",
default=False,
help="Long output for debugging")
#parser.add_option("--MPI" , action="store_true", default=False, help="use MPI version")
#parser.add_option("--radiuspca", type="int" , default=-1 , help="radius for PCA" )
#parser.add_option("--iter", type="int" , default=40 , help="maximum number of iterations (stop criterion of reconstruction process)" )
#parser.add_option("--abs", type="float" , default=0.0 , help="minimum average absolute change of voxels' values (stop criterion of reconstruction process)" )
#parser.add_option("--squ", type="float" , default=0.0 , help="minimum average squared change of voxels' values (stop criterion of reconstruction process)" )
parser.add_option(
"--VAR",
action="store_true",
default=False,
help="stack on input consists of 2D variances (Default False)")
parser.add_option(
"--decimate",
type="float",
default=1.0,
help=
"image decimate rate, a number larger (expand image) or less (shrink image) than 1. default is 1"
)
parser.add_option(
"--window",
type="int",
default=0,
help=
"reduce images to a small image size without changing pixel_size. Default value is zero."
)
#parser.add_option("--SND", action="store_true", default=False, help="compute squared normalized differences (Default False)")
parser.add_option(
"--nvec",
type="int",
default=0,
help="number of eigenvectors, default = 0 meaning no PCA calculated")
parser.add_option(
"--symmetrize",
action="store_true",
default=False,
help="Prepare input stack for handling symmetry (Default False)")
(options, args) = parser.parse_args()
#####
from mpi import mpi_init, mpi_comm_rank, mpi_comm_size, mpi_recv, MPI_COMM_WORLD
from mpi import mpi_barrier, mpi_reduce, mpi_bcast, mpi_send, MPI_FLOAT, MPI_SUM, MPI_INT, MPI_MAX
from applications import MPI_start_end
from reconstruction import recons3d_em, recons3d_em_MPI
from reconstruction import recons3d_4nn_MPI, recons3d_4nn_ctf_MPI
from utilities import print_begin_msg, print_end_msg, print_msg
from utilities import read_text_row, get_image, get_im
from utilities import bcast_EMData_to_all, bcast_number_to_all
from utilities import get_symt
# This is code for handling symmetries by the above program. To be incorporated. PAP 01/27/2015
from EMAN2db import db_open_dict
# Set up global variables related to bdb cache
if global_def.CACHE_DISABLE:
from utilities import disable_bdb_cache
disable_bdb_cache()
# Set up global variables related to ERROR function
global_def.BATCH = True
# detect if program is running under MPI
RUNNING_UNDER_MPI = "OMPI_COMM_WORLD_SIZE" in os.environ
if RUNNING_UNDER_MPI:
global_def.MPI = True
if options.symmetrize:
if RUNNING_UNDER_MPI:
try:
sys.argv = mpi_init(len(sys.argv), sys.argv)
try:
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
if (number_of_proc > 1):
ERROR(
"Cannot use more than one CPU for symmetry prepration",
"sx3dvariability", 1)
except:
pass
except:
pass
if options.output_dir != "./" and not os.path.exists(
options.output_dir):
os.mkdir(options.output_dir)
# Input
#instack = "Clean_NORM_CTF_start_wparams.hdf"
#instack = "bdb:data"
from logger import Logger, BaseLogger_Files
if os.path.exists(os.path.join(options.output_dir, "log.txt")):
os.remove(os.path.join(options.output_dir, "log.txt"))
log_main = Logger(BaseLogger_Files())
log_main.prefix = os.path.join(options.output_dir, "./")
instack = args[0]
sym = options.sym.lower()
if (sym == "c1"):
ERROR("There is no need to symmetrize stack for C1 symmetry",
"sx3dvariability", 1)
line = ""
for a in sys.argv:
line += " " + a
log_main.add(line)
if (instack[:4] != "bdb:"):
if output_dir == "./": stack = "bdb:data"
else: stack = "bdb:" + options.output_dir + "/data"
delete_bdb(stack)
junk = cmdexecute("sxcpy.py " + instack + " " + stack)
else:
stack = instack
qt = EMUtil.get_all_attributes(stack, 'xform.projection')
na = len(qt)
ts = get_symt(sym)
ks = len(ts)
angsa = [None] * na
for k in xrange(ks):
#Qfile = "Q%1d"%k
if options.output_dir != "./":
Qfile = os.path.join(options.output_dir, "Q%1d" % k)
else:
Qfile = os.path.join(options.output_dir, "Q%1d" % k)
#delete_bdb("bdb:Q%1d"%k)
delete_bdb("bdb:" + Qfile)
#junk = cmdexecute("e2bdb.py "+stack+" --makevstack=bdb:Q%1d"%k)
junk = cmdexecute("e2bdb.py " + stack + " --makevstack=bdb:" +
Qfile)
#DB = db_open_dict("bdb:Q%1d"%k)
DB = db_open_dict("bdb:" + Qfile)
for i in xrange(na):
ut = qt[i] * ts[k]
DB.set_attr(i, "xform.projection", ut)
#bt = ut.get_params("spider")
#angsa[i] = [round(bt["phi"],3)%360.0, round(bt["theta"],3)%360.0, bt["psi"], -bt["tx"], -bt["ty"]]
#write_text_row(angsa, 'ptsma%1d.txt'%k)
#junk = cmdexecute("e2bdb.py "+stack+" --makevstack=bdb:Q%1d"%k)
#junk = cmdexecute("sxheader.py bdb:Q%1d --params=xform.projection --import=ptsma%1d.txt"%(k,k))
DB.close()
if options.output_dir == "./": delete_bdb("bdb:sdata")
else: delete_bdb("bdb:" + options.output_dir + "/" + "sdata")
#junk = cmdexecute("e2bdb.py . --makevstack=bdb:sdata --filt=Q")
sdata = "bdb:" + options.output_dir + "/" + "sdata"
print(sdata)
junk = cmdexecute("e2bdb.py " + options.output_dir +
" --makevstack=" + sdata + " --filt=Q")
#junk = cmdexecute("ls EMAN2DB/sdata*")
#a = get_im("bdb:sdata")
a = get_im(sdata)
a.set_attr("variabilitysymmetry", sym)
#a.write_image("bdb:sdata")
a.write_image(sdata)
else:
sys.argv = mpi_init(len(sys.argv), sys.argv)
myid = mpi_comm_rank(MPI_COMM_WORLD)
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
main_node = 0
if len(args) == 1:
stack = args[0]
else:
print(("usage: " + usage))
print(("Please run '" + progname + " -h' for detailed options"))
return 1
t0 = time()
# obsolete flags
options.MPI = True
options.nvec = 0
options.radiuspca = -1
options.iter = 40
options.abs = 0.0
options.squ = 0.0
if options.fl > 0.0 and options.aa == 0.0:
ERROR("Fall off has to be given for the low-pass filter",
"sx3dvariability", 1, myid)
if options.VAR and options.SND:
ERROR("Only one of var and SND can be set!", "sx3dvariability",
myid)
exit()
if options.VAR and (options.ave2D or options.ave3D or options.var2D):
ERROR(
"When VAR is set, the program cannot output ave2D, ave3D or var2D",
"sx3dvariability", 1, myid)
exit()
#if options.SND and (options.ave2D or options.ave3D):
# ERROR("When SND is set, the program cannot output ave2D or ave3D", "sx3dvariability", 1, myid)
# exit()
if options.nvec > 0:
ERROR("PCA option not implemented", "sx3dvariability", 1, myid)
exit()
if options.nvec > 0 and options.ave3D == None:
ERROR("When doing PCA analysis, one must set ave3D",
"sx3dvariability",
myid=myid)
exit()
import string
options.sym = options.sym.lower()
# if global_def.CACHE_DISABLE:
# from utilities import disable_bdb_cache
# disable_bdb_cache()
# global_def.BATCH = True
if myid == main_node:
if options.output_dir != "./" and not os.path.exists(
options.output_dir):
os.mkdir(options.output_dir)
img_per_grp = options.img_per_grp
nvec = options.nvec
radiuspca = options.radiuspca
from logger import Logger, BaseLogger_Files
#if os.path.exists(os.path.join(options.output_dir, "log.txt")): os.remove(os.path.join(options.output_dir, "log.txt"))
log_main = Logger(BaseLogger_Files())
log_main.prefix = os.path.join(options.output_dir, "./")
if myid == main_node:
line = ""
for a in sys.argv:
line += " " + a
log_main.add(line)
log_main.add("-------->>>Settings given by all options<<<-------")
log_main.add("instack :" + stack)
log_main.add("output_dir :" + options.output_dir)
log_main.add("var3d :" + options.var3D)
if myid == main_node:
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
#print_begin_msg("sx3dvariability")
msg = "sx3dvariability"
log_main.add(msg)
print(line, msg)
msg = ("%-70s: %s\n" % ("Input stack", stack))
log_main.add(msg)
print(line, msg)
symbaselen = 0
if myid == main_node:
nima = EMUtil.get_image_count(stack)
img = get_image(stack)
nx = img.get_xsize()
ny = img.get_ysize()
if options.sym != "c1":
imgdata = get_im(stack)
try:
i = imgdata.get_attr("variabilitysymmetry").lower()
if (i != options.sym):
ERROR(
"The symmetry provided does not agree with the symmetry of the input stack",
"sx3dvariability",
myid=myid)
except:
ERROR(
"Input stack is not prepared for symmetry, please follow instructions",
"sx3dvariability",
myid=myid)
from utilities import get_symt
i = len(get_symt(options.sym))
if ((nima / i) * i != nima):
ERROR(
"The length of the input stack is incorrect for symmetry processing",
"sx3dvariability",
myid=myid)
symbaselen = nima / i
else:
symbaselen = nima
else:
nima = 0
nx = 0
ny = 0
nima = bcast_number_to_all(nima)
nx = bcast_number_to_all(nx)
ny = bcast_number_to_all(ny)
Tracker = {}
Tracker["total_stack"] = nima
if options.decimate == 1.:
if options.window != 0:
nx = options.window
ny = options.window
else:
if options.window == 0:
nx = int(nx * options.decimate)
ny = int(ny * options.decimate)
else:
nx = int(options.window * options.decimate)
ny = nx
Tracker["nx"] = nx
Tracker["ny"] = ny
Tracker["nz"] = nx
symbaselen = bcast_number_to_all(symbaselen)
if radiuspca == -1: radiuspca = nx / 2 - 2
if myid == main_node:
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
msg = "%-70s: %d\n" % ("Number of projection", nima)
log_main.add(msg)
print(line, msg)
img_begin, img_end = MPI_start_end(nima, number_of_proc, myid)
"""
if options.SND:
from projection import prep_vol, prgs
from statistics import im_diff
from utilities import get_im, model_circle, get_params_proj, set_params_proj
from utilities import get_ctf, generate_ctf
from filter import filt_ctf
imgdata = EMData.read_images(stack, range(img_begin, img_end))
if options.CTF:
vol = recons3d_4nn_ctf_MPI(myid, imgdata, 1.0, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
else:
vol = recons3d_4nn_MPI(myid, imgdata, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
bcast_EMData_to_all(vol, myid)
volft, kb = prep_vol(vol)
mask = model_circle(nx/2-2, nx, ny)
varList = []
for i in xrange(img_begin, img_end):
phi, theta, psi, s2x, s2y = get_params_proj(imgdata[i-img_begin])
ref_prj = prgs(volft, kb, [phi, theta, psi, -s2x, -s2y])
if options.CTF:
ctf_params = get_ctf(imgdata[i-img_begin])
ref_prj = filt_ctf(ref_prj, generate_ctf(ctf_params))
diff, A, B = im_diff(ref_prj, imgdata[i-img_begin], mask)
diff2 = diff*diff
set_params_proj(diff2, [phi, theta, psi, s2x, s2y])
varList.append(diff2)
mpi_barrier(MPI_COMM_WORLD)
"""
if options.VAR:
#varList = EMData.read_images(stack, range(img_begin, img_end))
varList = []
this_image = EMData()
for index_of_particle in xrange(img_begin, img_end):
this_image.read_image(stack, index_of_particle)
varList.append(
image_decimate_window_xform_ctf(this_image,
options.decimate,
options.window,
options.CTF))
else:
from utilities import bcast_number_to_all, bcast_list_to_all, send_EMData, recv_EMData
from utilities import set_params_proj, get_params_proj, params_3D_2D, get_params2D, set_params2D, compose_transform2
from utilities import model_blank, nearest_proj, model_circle
from applications import pca
from statistics import avgvar, avgvar_ctf, ccc
from filter import filt_tanl
from morphology import threshold, square_root
from projection import project, prep_vol, prgs
from sets import Set
if myid == main_node:
t1 = time()
proj_angles = []
aveList = []
tab = EMUtil.get_all_attributes(stack, 'xform.projection')
for i in xrange(nima):
t = tab[i].get_params('spider')
phi = t['phi']
theta = t['theta']
psi = t['psi']
x = theta
if x > 90.0: x = 180.0 - x
x = x * 10000 + psi
proj_angles.append([x, t['phi'], t['theta'], t['psi'], i])
t2 = time()
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
msg = "%-70s: %d\n" % ("Number of neighboring projections",
img_per_grp)
log_main.add(msg)
print(line, msg)
msg = "...... Finding neighboring projections\n"
log_main.add(msg)
print(line, msg)
if options.VERBOSE:
msg = "Number of images per group: %d" % img_per_grp
log_main.add(msg)
print(line, msg)
msg = "Now grouping projections"
log_main.add(msg)
print(line, msg)
proj_angles.sort()
proj_angles_list = [0.0] * (nima * 4)
if myid == main_node:
for i in xrange(nima):
proj_angles_list[i * 4] = proj_angles[i][1]
proj_angles_list[i * 4 + 1] = proj_angles[i][2]
proj_angles_list[i * 4 + 2] = proj_angles[i][3]
proj_angles_list[i * 4 + 3] = proj_angles[i][4]
proj_angles_list = bcast_list_to_all(proj_angles_list, myid,
main_node)
proj_angles = []
for i in xrange(nima):
proj_angles.append([
proj_angles_list[i * 4], proj_angles_list[i * 4 + 1],
proj_angles_list[i * 4 + 2],
int(proj_angles_list[i * 4 + 3])
])
del proj_angles_list
proj_list, mirror_list = nearest_proj(proj_angles, img_per_grp,
range(img_begin, img_end))
all_proj = Set()
for im in proj_list:
for jm in im:
all_proj.add(proj_angles[jm][3])
all_proj = list(all_proj)
if options.VERBOSE:
print("On node %2d, number of images needed to be read = %5d" %
(myid, len(all_proj)))
index = {}
for i in xrange(len(all_proj)):
index[all_proj[i]] = i
mpi_barrier(MPI_COMM_WORLD)
if myid == main_node:
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
msg = ("%-70s: %.2f\n" %
("Finding neighboring projections lasted [s]",
time() - t2))
log_main.add(msg)
print(msg)
msg = ("%-70s: %d\n" %
("Number of groups processed on the main node",
len(proj_list)))
log_main.add(msg)
print(line, msg)
if options.VERBOSE:
print("Grouping projections took: ", (time() - t2) / 60,
"[min]")
print("Number of groups on main node: ", len(proj_list))
mpi_barrier(MPI_COMM_WORLD)
if myid == main_node:
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
msg = ("...... calculating the stack of 2D variances \n")
log_main.add(msg)
print(line, msg)
if options.VERBOSE:
print("Now calculating the stack of 2D variances")
proj_params = [0.0] * (nima * 5)
aveList = []
varList = []
if nvec > 0:
eigList = [[] for i in xrange(nvec)]
if options.VERBOSE:
print("Begin to read images on processor %d" % (myid))
ttt = time()
#imgdata = EMData.read_images(stack, all_proj)
imgdata = []
for index_of_proj in xrange(len(all_proj)):
#img = EMData()
#img.read_image(stack, all_proj[index_of_proj])
dmg = image_decimate_window_xform_ctf(
get_im(stack, all_proj[index_of_proj]), options.decimate,
options.window, options.CTF)
#print dmg.get_xsize(), "init"
imgdata.append(dmg)
if options.VERBOSE:
print("Reading images on processor %d done, time = %.2f" %
(myid, time() - ttt))
print("On processor %d, we got %d images" %
(myid, len(imgdata)))
mpi_barrier(MPI_COMM_WORLD)
'''
imgdata2 = EMData.read_images(stack, range(img_begin, img_end))
if options.fl > 0.0:
for k in xrange(len(imgdata2)):
imgdata2[k] = filt_tanl(imgdata2[k], options.fl, options.aa)
if options.CTF:
vol = recons3d_4nn_ctf_MPI(myid, imgdata2, 1.0, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
else:
vol = recons3d_4nn_MPI(myid, imgdata2, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
if myid == main_node:
vol.write_image("vol_ctf.hdf")
print_msg("Writing to the disk volume reconstructed from averages as : %s\n"%("vol_ctf.hdf"))
del vol, imgdata2
mpi_barrier(MPI_COMM_WORLD)
'''
from applications import prepare_2d_forPCA
from utilities import model_blank
for i in xrange(len(proj_list)):
ki = proj_angles[proj_list[i][0]][3]
if ki >= symbaselen: continue
mi = index[ki]
phiM, thetaM, psiM, s2xM, s2yM = get_params_proj(imgdata[mi])
grp_imgdata = []
for j in xrange(img_per_grp):
mj = index[proj_angles[proj_list[i][j]][3]]
phi, theta, psi, s2x, s2y = get_params_proj(imgdata[mj])
alpha, sx, sy, mirror = params_3D_2D_NEW(
phi, theta, psi, s2x, s2y, mirror_list[i][j])
if thetaM <= 90:
if mirror == 0:
alpha, sx, sy, scale = compose_transform2(
alpha, sx, sy, 1.0, phiM - phi, 0.0, 0.0, 1.0)
else:
alpha, sx, sy, scale = compose_transform2(
alpha, sx, sy, 1.0, 180 - (phiM - phi), 0.0,
0.0, 1.0)
else:
if mirror == 0:
alpha, sx, sy, scale = compose_transform2(
alpha, sx, sy, 1.0, -(phiM - phi), 0.0, 0.0,
1.0)
else:
alpha, sx, sy, scale = compose_transform2(
alpha, sx, sy, 1.0, -(180 - (phiM - phi)), 0.0,
0.0, 1.0)
set_params2D(imgdata[mj], [alpha, sx, sy, mirror, 1.0])
grp_imgdata.append(imgdata[mj])
#print grp_imgdata[j].get_xsize(), imgdata[mj].get_xsize()
if not options.no_norm:
#print grp_imgdata[j].get_xsize()
mask = model_circle(nx / 2 - 2, nx, nx)
for k in xrange(img_per_grp):
ave, std, minn, maxx = Util.infomask(
grp_imgdata[k], mask, False)
grp_imgdata[k] -= ave
grp_imgdata[k] /= std
del mask
if options.fl > 0.0:
from filter import filt_ctf, filt_table
from fundamentals import fft, window2d
nx2 = 2 * nx
ny2 = 2 * ny
if options.CTF:
from utilities import pad
for k in xrange(img_per_grp):
grp_imgdata[k] = window2d(
fft(
filt_tanl(
filt_ctf(
fft(
pad(grp_imgdata[k], nx2, ny2,
1, 0.0)),
grp_imgdata[k].get_attr("ctf"),
binary=1), options.fl,
options.aa)), nx, ny)
#grp_imgdata[k] = window2d(fft( filt_table( filt_tanl( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1), options.fl, options.aa), fifi) ),nx,ny)
#grp_imgdata[k] = filt_tanl(grp_imgdata[k], options.fl, options.aa)
else:
for k in xrange(img_per_grp):
grp_imgdata[k] = filt_tanl(grp_imgdata[k],
options.fl, options.aa)
#grp_imgdata[k] = window2d(fft( filt_table( filt_tanl( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1), options.fl, options.aa), fifi) ),nx,ny)
#grp_imgdata[k] = filt_tanl(grp_imgdata[k], options.fl, options.aa)
else:
from utilities import pad, read_text_file
from filter import filt_ctf, filt_table
from fundamentals import fft, window2d
nx2 = 2 * nx
ny2 = 2 * ny
if options.CTF:
from utilities import pad
for k in xrange(img_per_grp):
grp_imgdata[k] = window2d(
fft(
filt_ctf(fft(
pad(grp_imgdata[k], nx2, ny2, 1, 0.0)),
grp_imgdata[k].get_attr("ctf"),
binary=1)), nx, ny)
#grp_imgdata[k] = window2d(fft( filt_table( filt_tanl( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1), options.fl, options.aa), fifi) ),nx,ny)
#grp_imgdata[k] = filt_tanl(grp_imgdata[k], options.fl, options.aa)
'''
if i < 10 and myid == main_node:
for k in xrange(10):
grp_imgdata[k].write_image("grp%03d.hdf"%i, k)
'''
"""
if myid == main_node and i==0:
for pp in xrange(len(grp_imgdata)):
grp_imgdata[pp].write_image("pp.hdf", pp)
"""
ave, grp_imgdata = prepare_2d_forPCA(grp_imgdata)
"""
if myid == main_node and i==0:
for pp in xrange(len(grp_imgdata)):
grp_imgdata[pp].write_image("qq.hdf", pp)
"""
var = model_blank(nx, ny)
for q in grp_imgdata:
Util.add_img2(var, q)
Util.mul_scalar(var, 1.0 / (len(grp_imgdata) - 1))
# Switch to std dev
var = square_root(threshold(var))
#if options.CTF: ave, var = avgvar_ctf(grp_imgdata, mode="a")
#else: ave, var = avgvar(grp_imgdata, mode="a")
"""
if myid == main_node:
ave.write_image("avgv.hdf",i)
var.write_image("varv.hdf",i)
"""
set_params_proj(ave, [phiM, thetaM, 0.0, 0.0, 0.0])
set_params_proj(var, [phiM, thetaM, 0.0, 0.0, 0.0])
aveList.append(ave)
varList.append(var)
if options.VERBOSE:
print("%5.2f%% done on processor %d" %
(i * 100.0 / len(proj_list), myid))
if nvec > 0:
eig = pca(input_stacks=grp_imgdata,
subavg="",
mask_radius=radiuspca,
nvec=nvec,
incore=True,
shuffle=False,
genbuf=True)
for k in xrange(nvec):
set_params_proj(eig[k], [phiM, thetaM, 0.0, 0.0, 0.0])
eigList[k].append(eig[k])
"""
if myid == 0 and i == 0:
for k in xrange(nvec):
eig[k].write_image("eig.hdf", k)
"""
del imgdata
# To this point, all averages, variances, and eigenvectors are computed
if options.ave2D:
from fundamentals import fpol
if myid == main_node:
km = 0
for i in xrange(number_of_proc):
if i == main_node:
for im in xrange(len(aveList)):
aveList[im].write_image(
os.path.join(options.output_dir,
options.ave2D), km)
km += 1
else:
nl = mpi_recv(1, MPI_INT, i,
SPARX_MPI_TAG_UNIVERSAL,
MPI_COMM_WORLD)
nl = int(nl[0])
for im in xrange(nl):
ave = recv_EMData(i, im + i + 70000)
"""
nm = mpi_recv(1, MPI_INT, i, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
nm = int(nm[0])
members = mpi_recv(nm, MPI_INT, i, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
ave.set_attr('members', map(int, members))
members = mpi_recv(nm, MPI_FLOAT, i, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
ave.set_attr('pix_err', map(float, members))
members = mpi_recv(3, MPI_FLOAT, i, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
ave.set_attr('refprojdir', map(float, members))
"""
tmpvol = fpol(ave, Tracker["nx"],
Tracker["nx"], 1)
tmpvol.write_image(
os.path.join(options.output_dir,
options.ave2D), km)
km += 1
else:
mpi_send(len(aveList), 1, MPI_INT, main_node,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
for im in xrange(len(aveList)):
send_EMData(aveList[im], main_node, im + myid + 70000)
"""
members = aveList[im].get_attr('members')
mpi_send(len(members), 1, MPI_INT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
mpi_send(members, len(members), MPI_INT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
members = aveList[im].get_attr('pix_err')
mpi_send(members, len(members), MPI_FLOAT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
try:
members = aveList[im].get_attr('refprojdir')
mpi_send(members, 3, MPI_FLOAT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
except:
mpi_send([-999.0,-999.0,-999.0], 3, MPI_FLOAT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
"""
if options.ave3D:
from fundamentals import fpol
if options.VERBOSE:
print("Reconstructing 3D average volume")
ave3D = recons3d_4nn_MPI(myid,
aveList,
symmetry=options.sym,
npad=options.npad)
bcast_EMData_to_all(ave3D, myid)
if myid == main_node:
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
ave3D = fpol(ave3D, Tracker["nx"], Tracker["nx"],
Tracker["nx"])
ave3D.write_image(
os.path.join(options.output_dir, options.ave3D))
msg = ("%-70s: %s\n" % (
"Writing to the disk volume reconstructed from averages as",
options.ave3D))
log_main.add(msg)
print(line, msg)
del ave, var, proj_list, stack, phi, theta, psi, s2x, s2y, alpha, sx, sy, mirror, aveList
if nvec > 0:
for k in xrange(nvec):
if options.VERBOSE:
print("Reconstruction eigenvolumes", k)
cont = True
ITER = 0
mask2d = model_circle(radiuspca, nx, nx)
while cont:
#print "On node %d, iteration %d"%(myid, ITER)
eig3D = recons3d_4nn_MPI(myid,
eigList[k],
symmetry=options.sym,
npad=options.npad)
bcast_EMData_to_all(eig3D, myid, main_node)
if options.fl > 0.0:
eig3D = filt_tanl(eig3D, options.fl, options.aa)
if myid == main_node:
eig3D.write_image(
os.path.join(options.outpout_dir,
"eig3d_%03d.hdf" % (k, ITER)))
Util.mul_img(eig3D,
model_circle(radiuspca, nx, nx, nx))
eig3Df, kb = prep_vol(eig3D)
del eig3D
cont = False
icont = 0
for l in xrange(len(eigList[k])):
phi, theta, psi, s2x, s2y = get_params_proj(
eigList[k][l])
proj = prgs(eig3Df, kb,
[phi, theta, psi, s2x, s2y])
cl = ccc(proj, eigList[k][l], mask2d)
if cl < 0.0:
icont += 1
cont = True
eigList[k][l] *= -1.0
u = int(cont)
u = mpi_reduce([u], 1, MPI_INT, MPI_MAX, main_node,
MPI_COMM_WORLD)
icont = mpi_reduce([icont], 1, MPI_INT, MPI_SUM,
main_node, MPI_COMM_WORLD)
if myid == main_node:
line = strftime("%Y-%m-%d_%H:%M:%S",
localtime()) + " =>"
u = int(u[0])
msg = (" Eigenvector: ", k, " number changed ",
int(icont[0]))
log_main.add(msg)
print(line, msg)
else:
u = 0
u = bcast_number_to_all(u, main_node)
cont = bool(u)
ITER += 1
del eig3Df, kb
mpi_barrier(MPI_COMM_WORLD)
del eigList, mask2d
if options.ave3D: del ave3D
if options.var2D:
from fundamentals import fpol
if myid == main_node:
km = 0
for i in xrange(number_of_proc):
if i == main_node:
for im in xrange(len(varList)):
tmpvol = fpol(varList[im], Tracker["nx"],
Tracker["nx"], 1)
tmpvol.write_image(
os.path.join(options.output_dir,
options.var2D), km)
km += 1
else:
nl = mpi_recv(1, MPI_INT, i,
SPARX_MPI_TAG_UNIVERSAL,
MPI_COMM_WORLD)
nl = int(nl[0])
for im in xrange(nl):
ave = recv_EMData(i, im + i + 70000)
tmpvol = fpol(ave, Tracker["nx"],
Tracker["nx"], 1)
tmpvol.write_image(
os.path.join(options.output_dir,
options.var2D, km))
km += 1
else:
mpi_send(len(varList), 1, MPI_INT, main_node,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
for im in xrange(len(varList)):
send_EMData(varList[im], main_node, im + myid +
70000) # What with the attributes??
mpi_barrier(MPI_COMM_WORLD)
if options.var3D:
if myid == main_node and options.VERBOSE:
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
msg = ("Reconstructing 3D variability volume")
log_main.add(msg)
print(line, msg)
t6 = time()
# radiusvar = options.radius
# if( radiusvar < 0 ): radiusvar = nx//2 -3
res = recons3d_4nn_MPI(myid,
varList,
symmetry=options.sym,
npad=options.npad)
#res = recons3d_em_MPI(varList, vol_stack, options.iter, radiusvar, options.abs, True, options.sym, options.squ)
if myid == main_node:
from fundamentals import fpol
res = fpol(res, Tracker["nx"], Tracker["nx"], Tracker["nx"])
res.write_image(os.path.join(options.output_dir,
options.var3D))
if myid == main_node:
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
msg = ("%-70s: %.2f\n" %
("Reconstructing 3D variability took [s]", time() - t6))
log_main.add(msg)
print(line, msg)
if options.VERBOSE:
print("Reconstruction took: %.2f [min]" %
((time() - t6) / 60))
if myid == main_node:
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
msg = ("%-70s: %.2f\n" %
("Total time for these computations [s]", time() - t0))
print(line, msg)
log_main.add(msg)
if options.VERBOSE:
print("Total time for these computations: %.2f [min]" %
((time() - t0) / 60))
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
msg = ("sx3dvariability")
print(line, msg)
log_main.add(msg)
from mpi import mpi_finalize
mpi_finalize()
if RUNNING_UNDER_MPI:
global_def.MPI = False
global_def.BATCH = False
def main():
import sys
import os
import math
import random
import pyemtbx.options
import time
from random import random, seed, randint
from optparse import OptionParser
progname = os.path.basename(sys.argv[0])
usage = progname + """ [options] <inputfile> <outputfile>
Generic 2-D image processing programs.
Functionality:
1. Phase flip a stack of images and write output to new file:
sxprocess.py input_stack.hdf output_stack.hdf --phase_flip
2. Resample (decimate or interpolate up) images (2D or 3D) in a stack to change the pixel size.
The window size will change accordingly.
sxprocess input.hdf output.hdf --changesize --ratio=0.5
3. Compute average power spectrum of a stack of 2D images with optional padding (option wn) with zeroes or a 3-D volume.
sxprocess.py input_stack.hdf powerspectrum.hdf --pw [--wn=1024]
4. Generate a stack of projections bdb:data and micrographs with prefix mic (i.e., mic0.hdf, mic1.hdf etc) from structure input_structure.hdf, with CTF applied to both projections and micrographs:
sxprocess.py input_structure.hdf data mic --generate_projections format="bdb":apix=5.2:CTF=True:boxsize=64
5. Retrieve original image numbers in the selected ISAC group (here group 12 from generation 3):
sxprocess.py bdb:test3 class_averages_generation_3.hdf list3_12.txt --isacgroup=12 --params=originalid
6. Retrieve original image numbers of images listed in ISAC output stack of averages:
sxprocess.py select1.hdf ohk.txt
7. Adjust rotationally averaged power spectrum of an image to that of a reference image or a reference 1D power spectrum stored in an ASCII file.
Optionally use a tangent low-pass filter. Also works for a stack of images, in which case the output is also a stack.
sxprocess.py vol.hdf ref.hdf avol.hdf < 0.25 0.2> --adjpw
sxprocess.py vol.hdf pw.txt avol.hdf < 0.25 0.2> --adjpw
8. Generate a 1D rotationally averaged power spectrum of an image.
sxprocess.py vol.hdf --rotwp=rotpw.txt
# Output will contain three columns:
(1) rotationally averaged power spectrum
(2) logarithm of the rotationally averaged power spectrum
(3) integer line number (from zero to approximately to half the image size)
9. Apply 3D transformation (rotation and/or shift) to a set of orientation parameters associated with projection data.
sxprocess.py --transfromparams=phi,theta,psi,tx,ty,tz input.txt output.txt
The output file is then imported and 3D transformed volume computed:
sxheader.py bdb:p --params=xform.projection --import=output.txt
mpirun -np 2 sxrecons3d_n.py bdb:p tvol.hdf --MPI
The reconstructed volume is in the position of the volume computed using the input.txt parameters and then
transformed with rot_shift3D(vol, phi,theta,psi,tx,ty,tz)
10. Import ctf parameters from the output of sxcter into windowed particle headers.
There are three possible input files formats: (1) all particles are in one stack, (2 aor 3) particles are in stacks, each stack corresponds to a single micrograph.
In each case the particles should contain a name of the micrograph of origin stores using attribute name 'ptcl_source_image'.
Normally this is done by e2boxer.py during windowing.
Particles whose defocus or astigmatism error exceed set thresholds will be skipped, otherwise, virtual stacks with the original way preceded by G will be created.
sxprocess.py --input=bdb:data --importctf=outdir/partres --defocuserror=10.0 --astigmatismerror=5.0
# Output will be a vritual stack bdb:Gdata
sxprocess.py --input="bdb:directory/stacks*" --importctf=outdir/partres --defocuserror=10.0 --astigmatismerror=5.0
To concatenate output files:
cd directory
e2bdb.py . --makevstack=bdb:allparticles --filt=G
IMPORTANT: Please do not move (or remove!) any input/intermediate EMAN2DB files as the information is linked between them.
11. Scale 3D shifts. The shifts in the input five columns text file with 3D orientation parameters will be DIVIDED by the scale factor
sxprocess.py orientationparams.txt scaledparams.txt scale=0.5
12. Generate 3D mask from a given 3-D volume automatically or using threshold provided by user.
13. Postprocess 3-D or 2-D images:
for 3-D volumes: calculate FSC with provided mask; weight summed volume with FSC; estimate B-factor from FSC weighted summed two volumes; apply negative B-factor to the weighted volume.
for 2-D images: calculate B-factor and apply negative B-factor to 2-D images.
14. Winow stack file -reduce size of images without changing the pixel size.
"""
parser = OptionParser(usage,version=SPARXVERSION)
parser.add_option("--order", action="store_true", help="Two arguments are required: name of input stack and desired name of output stack. The output stack is the input stack sorted by similarity in terms of cross-correlation coefficent.", default=False)
parser.add_option("--order_lookup", action="store_true", help="Test/Debug.", default=False)
parser.add_option("--order_metropolis", action="store_true", help="Test/Debug.", default=False)
parser.add_option("--order_pca", action="store_true", help="Test/Debug.", default=False)
parser.add_option("--initial", type="int", default=-1, help="Specifies which image will be used as an initial seed to form the chain. (default = 0, means the first image)")
parser.add_option("--circular", action="store_true", help="Select circular ordering (fisr image has to be similar to the last", default=False)
parser.add_option("--radius", type="int", default=-1, help="Radius of a circular mask for similarity based ordering")
parser.add_option("--changesize", action="store_true", help="resample (decimate or interpolate up) images (2D or 3D) in a stack to change the pixel size.", default=False)
parser.add_option("--ratio", type="float", default=1.0, help="The ratio of new to old image size (if <1 the pixel size will increase and image size decrease, if>1, the other way round")
parser.add_option("--pw", action="store_true", help="compute average power spectrum of a stack of 2-D images with optional padding (option wn) with zeroes", default=False)
parser.add_option("--wn", type="int", default=-1, help="Size of window to use (should be larger/equal than particle box size, default padding to max(nx,ny))")
parser.add_option("--phase_flip", action="store_true", help="Phase flip the input stack", default=False)
parser.add_option("--makedb", metavar="param1=value1:param2=value2", type="string",
action="append", help="One argument is required: name of key with which the database will be created. Fill in database with parameters specified as follows: --makedb param1=value1:param2=value2, e.g. 'gauss_width'=1.0:'pixel_input'=5.2:'pixel_output'=5.2:'thr_low'=1.0")
parser.add_option("--generate_projections", metavar="param1=value1:param2=value2", type="string",
action="append", help="Three arguments are required: name of input structure from which to generate projections, desired name of output projection stack, and desired prefix for micrographs (e.g. if prefix is 'mic', then micrographs mic0.hdf, mic1.hdf etc will be generated). Optional arguments specifying format, apix, box size and whether to add CTF effects can be entered as follows after --generate_projections: format='bdb':apix=5.2:CTF=True:boxsize=100, or format='hdf', etc., where format is bdb or hdf, apix (pixel size) is a float, CTF is True or False, and boxsize denotes the dimension of the box (assumed to be a square). If an optional parameter is not specified, it will default as follows: format='bdb', apix=2.5, CTF=False, boxsize=64.")
parser.add_option("--isacgroup", type="int", help="Retrieve original image numbers in the selected ISAC group. See ISAC documentation for details.", default=-1)
parser.add_option("--isacselect", action="store_true", help="Retrieve original image numbers of images listed in ISAC output stack of averages. See ISAC documentation for details.", default=False)
parser.add_option("--params", type="string", default=None, help="Name of header of parameter, which one depends on specific option")
parser.add_option("--adjpw", action="store_true", help="Adjust rotationally averaged power spectrum of an image", default=False)
parser.add_option("--rotpw", type="string", default=None, help="Name of the text file to contain rotationally averaged power spectrum of the input image.")
parser.add_option("--transformparams", type="string", default=None, help="Transform 3D projection orientation parameters using six 3D parameters (phi, theta,psi,sx,sy,sz). Input: --transformparams=45.,66.,12.,-2,3,-5.5 desired six transformation of the reconstructed structure. Output: file with modified orientation parameters.")
# import ctf estimates done using cter
parser.add_option("--input", type="string", default= None, help="Input particles.")
parser.add_option("--importctf", type="string", default= None, help="Name of the file containing CTF parameters produced by sxcter.")
parser.add_option("--defocuserror", type="float", default=1000000.0, help="Exclude micrographs whose relative defocus error as estimated by sxcter is larger than defocuserror percent. The error is computed as (std dev defocus)/defocus*100%")
parser.add_option("--astigmatismerror", type="float", default=360.0, help="Set to zero astigmatism for micrographs whose astigmatism angular error as estimated by sxcter is larger than astigmatismerror degrees.")
# import ctf estimates done using cter
parser.add_option("--scale", type="float", default=-1.0, help="Divide shifts in the input 3D orientation parameters text file by the scale factor.")
# generate adaptive mask from an given 3-D volume
parser.add_option("--adaptive_mask", action="store_true", help="create adavptive 3-D mask from a given volume", default=False)
parser.add_option("--nsigma", type="float", default= 1., help="number of times of sigma of the input volume to obtain the the large density cluster")
parser.add_option("--ndilation", type="int", default= 3, help="number of times of dilation applied to the largest cluster of density")
parser.add_option("--kernel_size", type="int", default= 11, help="convolution kernel for smoothing the edge of the mask")
parser.add_option("--gauss_standard_dev", type="int", default= 9, help="stanadard deviation value to generate Gaussian edge")
parser.add_option("--threshold", type="float", default= 9999., help="threshold provided by user to binarize input volume")
parser.add_option("--ne", type="int", default= 0, help="number of times to erode the binarized input image")
parser.add_option("--nd", type="int", default= 0, help="number of times to dilate the binarized input image")
parser.add_option("--postprocess", action="store_true", help="postprocess unfiltered odd, even 3-D volumes",default=False)
parser.add_option("--fsc_weighted", action="store_true", help="postprocess unfiltered odd, even 3-D volumes")
parser.add_option("--low_pass_filter", action="store_true", default=False, help="postprocess unfiltered odd, even 3-D volumes")
parser.add_option("--ff", type="float", default=.25, help="low pass filter stop band frequency in absolute unit")
parser.add_option("--aa", type="float", default=.1, help="low pass filter falloff" )
parser.add_option("--mask", type="string", help="input mask file", default=None)
parser.add_option("--output", type="string", help="output file name", default="postprocessed.hdf")
parser.add_option("--pixel_size", type="float", help="pixel size of the data", default=1.0)
parser.add_option("--B_start", type="float", help="starting frequency in Angstrom for B-factor estimation", default=10.)
parser.add_option("--FSC_cutoff", type="float", help="stop frequency in Angstrom for B-factor estimation", default=0.143)
parser.add_option("--2d", action="store_true", help="postprocess isac 2-D averaged images",default=False)
parser.add_option("--window_stack", action="store_true", help="window stack images using a smaller window size", default=False)
parser.add_option("--box", type="int", default= 0, help="the new window size ")
(options, args) = parser.parse_args()
global_def.BATCH = True
if options.phase_flip:
nargs = len(args)
if nargs != 2:
print "must provide name of input and output file!"
return
from EMAN2 import Processor
instack = args[0]
outstack = args[1]
nima = EMUtil.get_image_count(instack)
from filter import filt_ctf
for i in xrange(nima):
img = EMData()
img.read_image(instack, i)
try:
ctf = img.get_attr('ctf')
except:
print "no ctf information in input stack! Exiting..."
return
dopad = True
sign = 1
binary = 1 # phase flip
assert img.get_ysize() > 1
dict = ctf.to_dict()
dz = dict["defocus"]
cs = dict["cs"]
voltage = dict["voltage"]
pixel_size = dict["apix"]
b_factor = dict["bfactor"]
ampcont = dict["ampcont"]
dza = dict["dfdiff"]
azz = dict["dfang"]
if dopad and not img.is_complex(): ip = 1
else: ip = 0
params = {"filter_type": Processor.fourier_filter_types.CTF_,
"defocus" : dz,
"Cs": cs,
"voltage": voltage,
"Pixel_size": pixel_size,
"B_factor": b_factor,
"amp_contrast": ampcont,
"dopad": ip,
"binary": binary,
"sign": sign,
"dza": dza,
"azz":azz}
tmp = Processor.EMFourierFilter(img, params)
tmp.set_attr_dict({"ctf": ctf})
tmp.write_image(outstack, i)
elif options.changesize:
nargs = len(args)
if nargs != 2:
ERROR("must provide name of input and output file!", "change size", 1)
return
from utilities import get_im
instack = args[0]
outstack = args[1]
sub_rate = float(options.ratio)
nima = EMUtil.get_image_count(instack)
from fundamentals import resample
for i in xrange(nima):
resample(get_im(instack, i), sub_rate).write_image(outstack, i)
elif options.isacgroup>-1:
nargs = len(args)
if nargs != 3:
ERROR("Three files needed on input!", "isacgroup", 1)
return
from utilities import get_im
instack = args[0]
m=get_im(args[1],int(options.isacgroup)).get_attr("members")
l = []
for k in m:
l.append(int(get_im(args[0],k).get_attr(options.params)))
from utilities import write_text_file
write_text_file(l, args[2])
elif options.isacselect:
nargs = len(args)
if nargs != 2:
ERROR("Two files needed on input!", "isacgroup", 1)
return
from utilities import get_im
nima = EMUtil.get_image_count(args[0])
m = []
for k in xrange(nima):
m += get_im(args[0],k).get_attr("members")
m.sort()
from utilities import write_text_file
write_text_file(m, args[1])
elif options.pw:
nargs = len(args)
if nargs < 2:
ERROR("must provide name of input and output file!", "pw", 1)
return
from utilities import get_im, write_text_file
from fundamentals import rops_table
d = get_im(args[0])
ndim = d.get_ndim()
if ndim ==3:
pw = rops_table(d)
write_text_file(pw, args[1])
else:
nx = d.get_xsize()
ny = d.get_ysize()
if nargs ==3: mask = get_im(args[2])
wn = int(options.wn)
if wn == -1:
wn = max(nx, ny)
else:
if( (wn<nx) or (wn<ny) ): ERROR("window size cannot be smaller than the image size","pw",1)
n = EMUtil.get_image_count(args[0])
from utilities import model_blank, model_circle, pad
from EMAN2 import periodogram
p = model_blank(wn,wn)
for i in xrange(n):
d = get_im(args[0], i)
if nargs==3:
d *=mask
st = Util.infomask(d, None, True)
d -= st[0]
p += periodogram(pad(d, wn, wn, 1, 0.))
p /= n
p.write_image(args[1])
elif options.adjpw:
if len(args) < 3:
ERROR("filt_by_rops input target output fl aa (the last two are optional parameters of a low-pass filter)","adjpw",1)
return
img_stack = args[0]
from math import sqrt
from fundamentals import rops_table, fft
from utilities import read_text_file, get_im
from filter import filt_tanl, filt_table
if( args[1][-3:] == 'txt'):
rops_dst = read_text_file( args[1] )
else:
rops_dst = rops_table(get_im( args[1] ))
out_stack = args[2]
if(len(args) >4):
fl = float(args[3])
aa = float(args[4])
else:
fl = -1.0
aa = 0.0
nimage = EMUtil.get_image_count( img_stack )
for i in xrange(nimage):
img = fft(get_im(img_stack, i) )
rops_src = rops_table(img)
assert len(rops_dst) == len(rops_src)
table = [0.0]*len(rops_dst)
for j in xrange( len(rops_dst) ):
table[j] = sqrt( rops_dst[j]/rops_src[j] )
if( fl > 0.0):
img = filt_tanl(img, fl, aa)
img = fft(filt_table(img, table))
img.write_image(out_stack, i)
elif options.rotpw != None:
if len(args) != 1:
ERROR("Only one input permitted","rotpw",1)
return
from utilities import write_text_file, get_im
from fundamentals import rops_table
from math import log10
t = rops_table(get_im(args[0]))
x = range(len(t))
r = [0.0]*len(x)
for i in x: r[i] = log10(t[i])
write_text_file([t,r,x],options.rotpw)
elif options.transformparams != None:
if len(args) != 2:
ERROR("Please provide names of input and output files with orientation parameters","transformparams",1)
return
from utilities import read_text_row, write_text_row
transf = [0.0]*6
spl=options.transformparams.split(',')
for i in xrange(len(spl)): transf[i] = float(spl[i])
write_text_row( rotate_shift_params(read_text_row(args[0]), transf) , args[1])
elif options.makedb != None:
nargs = len(args)
if nargs != 1:
print "must provide exactly one argument denoting database key under which the input params will be stored"
return
dbkey = args[0]
print "database key under which params will be stored: ", dbkey
gbdb = js_open_dict("e2boxercache/gauss_box_DB.json")
parmstr = 'dummy:'+options.makedb[0]
(processorname, param_dict) = parsemodopt(parmstr)
dbdict = {}
for pkey in param_dict:
if (pkey == 'invert_contrast') or (pkey == 'use_variance'):
if param_dict[pkey] == 'True':
dbdict[pkey] = True
else:
dbdict[pkey] = False
else:
dbdict[pkey] = param_dict[pkey]
gbdb[dbkey] = dbdict
elif options.generate_projections:
nargs = len(args)
if nargs != 3:
ERROR("Must provide name of input structure(s) from which to generate projections, name of output projection stack, and prefix for output micrographs."\
"sxprocess - generate projections",1)
return
inpstr = args[0]
outstk = args[1]
micpref = args[2]
parmstr = 'dummy:'+options.generate_projections[0]
(processorname, param_dict) = parsemodopt(parmstr)
parm_CTF = False
parm_format = 'bdb'
parm_apix = 2.5
if 'CTF' in param_dict:
if param_dict['CTF'] == 'True':
parm_CTF = True
if 'format' in param_dict:
parm_format = param_dict['format']
if 'apix' in param_dict:
parm_apix = float(param_dict['apix'])
boxsize = 64
if 'boxsize' in param_dict:
boxsize = int(param_dict['boxsize'])
print "pixel size: ", parm_apix, " format: ", parm_format, " add CTF: ", parm_CTF, " box size: ", boxsize
scale_mult = 2500
sigma_add = 1.5
sigma_proj = 30.0
sigma2_proj = 17.5
sigma_gauss = 0.3
sigma_mic = 30.0
sigma2_mic = 17.5
sigma_gauss_mic = 0.3
if 'scale_mult' in param_dict:
scale_mult = float(param_dict['scale_mult'])
if 'sigma_add' in param_dict:
sigma_add = float(param_dict['sigma_add'])
if 'sigma_proj' in param_dict:
sigma_proj = float(param_dict['sigma_proj'])
if 'sigma2_proj' in param_dict:
sigma2_proj = float(param_dict['sigma2_proj'])
if 'sigma_gauss' in param_dict:
sigma_gauss = float(param_dict['sigma_gauss'])
if 'sigma_mic' in param_dict:
sigma_mic = float(param_dict['sigma_mic'])
if 'sigma2_mic' in param_dict:
sigma2_mic = float(param_dict['sigma2_mic'])
if 'sigma_gauss_mic' in param_dict:
sigma_gauss_mic = float(param_dict['sigma_gauss_mic'])
from filter import filt_gaussl, filt_ctf
from utilities import drop_spider_doc, even_angles, model_gauss, delete_bdb, model_blank,pad,model_gauss_noise,set_params2D, set_params_proj
from projection import prep_vol,prgs
seed(14567)
delta = 29
angles = even_angles(delta, 0.0, 89.9, 0.0, 359.9, "S")
nangle = len(angles)
modelvol = []
nvlms = EMUtil.get_image_count(inpstr)
from utilities import get_im
for k in xrange(nvlms): modelvol.append(get_im(inpstr,k))
nx = modelvol[0].get_xsize()
if nx != boxsize:
ERROR("Requested box dimension does not match dimension of the input model.", \
"sxprocess - generate projections",1)
nvol = 10
volfts = [[] for k in xrange(nvlms)]
for k in xrange(nvlms):
for i in xrange(nvol):
sigma = sigma_add + random() # 1.5-2.5
addon = model_gauss(sigma, boxsize, boxsize, boxsize, sigma, sigma, 38, 38, 40 )
scale = scale_mult * (0.5+random())
vf, kb = prep_vol(modelvol[k] + scale*addon)
volfts[k].append(vf)
del vf, modelvol
if parm_format == "bdb":
stack_data = "bdb:"+outstk
delete_bdb(stack_data)
else:
stack_data = outstk + ".hdf"
Cs = 2.0
pixel = parm_apix
voltage = 120.0
ampcont = 10.0
ibd = 4096/2-boxsize
iprj = 0
width = 240
xstart = 8 + boxsize/2
ystart = 8 + boxsize/2
rowlen = 17
from random import randint
params = []
for idef in xrange(3, 8):
irow = 0
icol = 0
mic = model_blank(4096, 4096)
defocus = idef * 0.5#0.2
if parm_CTF:
astampl=defocus*0.15
astangl=50.0
ctf = generate_ctf([defocus, Cs, voltage, pixel, ampcont, 0.0, astampl, astangl])
for i in xrange(nangle):
for k in xrange(12):
dphi = 8.0*(random()-0.5)
dtht = 8.0*(random()-0.5)
psi = 360.0*random()
phi = angles[i][0]+dphi
tht = angles[i][1]+dtht
s2x = 4.0*(random()-0.5)
s2y = 4.0*(random()-0.5)
params.append([phi, tht, psi, s2x, s2y])
ivol = iprj % nvol
#imgsrc = randint(0,nvlms-1)
imgsrc = iprj % nvlms
proj = prgs(volfts[imgsrc][ivol], kb, [phi, tht, psi, -s2x, -s2y])
x = xstart + irow * width
y = ystart + icol * width
mic += pad(proj, 4096, 4096, 1, 0.0, x-2048, y-2048, 0)
proj = proj + model_gauss_noise( sigma_proj, nx, nx )
if parm_CTF:
proj = filt_ctf(proj, ctf)
proj.set_attr_dict({"ctf":ctf, "ctf_applied":0})
proj = proj + filt_gaussl(model_gauss_noise(sigma2_proj, nx, nx), sigma_gauss)
proj.set_attr("origimgsrc",imgsrc)
proj.set_attr("test_id", iprj)
# flags describing the status of the image (1 = true, 0 = false)
set_params2D(proj, [0.0, 0.0, 0.0, 0, 1.0])
set_params_proj(proj, [phi, tht, psi, s2x, s2y])
proj.write_image(stack_data, iprj)
icol += 1
if icol == rowlen:
icol = 0
irow += 1
iprj += 1
mic += model_gauss_noise(sigma_mic,4096,4096)
if parm_CTF:
#apply CTF
mic = filt_ctf(mic, ctf)
mic += filt_gaussl(model_gauss_noise(sigma2_mic, 4096, 4096), sigma_gauss_mic)
mic.write_image(micpref + "%1d.hdf" % (idef-3), 0)
drop_spider_doc("params.txt", params)
elif options.importctf != None:
print ' IMPORTCTF '
from utilities import read_text_row,write_text_row
from random import randint
import subprocess
grpfile = 'groupid%04d'%randint(1000,9999)
ctfpfile = 'ctfpfile%04d'%randint(1000,9999)
cterr = [options.defocuserror/100.0, options.astigmatismerror]
ctfs = read_text_row(options.importctf)
for kk in xrange(len(ctfs)):
root,name = os.path.split(ctfs[kk][-1])
ctfs[kk][-1] = name[:-4]
if(options.input[:4] != 'bdb:'):
ERROR('Sorry, only bdb files implemented','importctf',1)
d = options.input[4:]
#try: str = d.index('*')
#except: str = -1
from string import split
import glob
uu = os.path.split(d)
uu = os.path.join(uu[0],'EMAN2DB',uu[1]+'.bdb')
flist = glob.glob(uu)
for i in xrange(len(flist)):
root,name = os.path.split(flist[i])
root = root[:-7]
name = name[:-4]
fil = 'bdb:'+os.path.join(root,name)
sourcemic = EMUtil.get_all_attributes(fil,'ptcl_source_image')
nn = len(sourcemic)
gctfp = []
groupid = []
for kk in xrange(nn):
junk,name2 = os.path.split(sourcemic[kk])
name2 = name2[:-4]
ctfp = [-1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]
for ll in xrange(len(ctfs)):
if(name2 == ctfs[ll][-1]):
# found correct
if(ctfs[ll][8]/ctfs[ll][0] <= cterr[0]):
# acceptable defocus error
ctfp = ctfs[ll][:8]
if(ctfs[ll][10] > cterr[1] ):
# error of astigmatism exceed the threshold, set astigmatism to zero.
ctfp[6] = 0.0
ctfp[7] = 0.0
gctfp.append(ctfp)
groupid.append(kk)
break
if(len(groupid) > 0):
write_text_row(groupid, grpfile)
write_text_row(gctfp, ctfpfile)
cmd = "{} {} {} {}".format('e2bdb.py',fil,'--makevstack=bdb:'+root+'G'+name,'--list='+grpfile)
#print cmd
subprocess.call(cmd, shell=True)
cmd = "{} {} {} {}".format('sxheader.py','bdb:'+root+'G'+name,'--params=ctf','--import='+ctfpfile)
#print cmd
subprocess.call(cmd, shell=True)
else:
print ' >>> Group ',name,' skipped.'
cmd = "{} {} {}".format("rm -f",grpfile,ctfpfile)
subprocess.call(cmd, shell=True)
elif options.scale > 0.0:
from utilities import read_text_row,write_text_row
scale = options.scale
nargs = len(args)
if nargs != 2:
print "Please provide names of input and output file!"
return
p = read_text_row(args[0])
for i in xrange(len(p)):
p[i][3] /= scale
p[i][4] /= scale
write_text_row(p, args[1])
elif options.adaptive_mask:
from utilities import get_im
from morphology import adaptive_mask, binarize, erosion, dilation
nsigma = options.nsigma
ndilation = options.ndilation
kernel_size = options.kernel_size
gauss_standard_dev = options.gauss_standard_dev
nargs = len(args)
if nargs ==0:
print " Create 3D mask from a given volume, either automatically or from the user provided threshold."
elif nargs > 2:
print "Too many inputs are given, try again!"
return
else:
inputvol = get_im(args[0])
input_path, input_file_name = os.path.split(args[0])
input_file_name_root,ext=os.path.splitext(input_file_name)
if nargs == 2: mask_file_name = args[1]
else: mask_file_name = "adaptive_mask_for_"+input_file_name_root+".hdf" # Only hdf file is output.
if options.threshold !=9999.:
mask3d = binarize(inputvol, options.threshold)
for i in xrange(options.ne): mask3d = erosion(mask3d)
for i in xrange(options.nd): mask3d = dilation(mask3d)
else:
mask3d = adaptive_mask(inputvol, nsigma, ndilation, kernel_size, gauss_standard_dev)
mask3d.write_image(mask_file_name)
elif options.postprocess:
from utilities import get_im
from fundamentals import rot_avg_table
from morphology import compute_bfactor,power
from statistics import fsc
from filter import filt_table, filt_gaussinv
from EMAN2 import periodogram
e1 = get_im(args[0],0)
if e1.get_zsize()==1:
nimage = EMUtil.get_image_count(args[0])
if options.mask !=None: m = get_im(options.mask)
else: m = None
for i in xrange(nimage):
e1 = get_im(args[0],i)
if m: e1 *=m
guinerline = rot_avg_table(power(periodogram(e1),.5))
freq_max = 1/(2.*pixel_size)
freq_min = 1./options.B_start
b,junk=compute_bfactor(guinerline, freq_min, freq_max, pixel_size)
tmp = b/pixel_size**2
sigma_of_inverse=sqrt(2./tmp)
e1 = filt_gaussinv(e1,sigma_of_inverse)
if options.low_pass_filter:
from filter import filt_tanl
e1 =filt_tanl(e1,options.ff, options.aa)
e1.write_image(options.output)
else:
nargs = len(args)
e1 = get_im(args[0])
if nargs >1: e2 = get_im(args[1])
if options.mask !=None: m = get_im(options.mask)
else: m =None
pixel_size = options.pixel_size
from math import sqrt
if m !=None:
e1 *=m
if nargs >1 :e2 *=m
if options.fsc_weighted:
frc = fsc(e1,e2,1)
## FSC is done on masked two images
#### FSC weighting sqrt((2.*fsc)/(1+fsc));
fil = len(frc[1])*[None]
for i in xrange(len(fil)):
if frc[1][i]>=options.FSC_cutoff: tmp = frc[1][i]
else: tmp = 0.0
fil[i] = sqrt(2.*tmp/(1.+tmp))
if nargs>1: e1 +=e2
if options.fsc_weighted: e1=filt_table(e1,fil)
guinerline = rot_avg_table(power(periodogram(e1),.5))
freq_max = 1/(2.*pixel_size)
freq_min = 1./options.B_start
b,junk = compute_bfactor(guinerline, freq_min, freq_max, pixel_size)
tmp = b/pixel_size**2
sigma_of_inverse=sqrt(2./tmp)
e1 = filt_gaussinv(e1,sigma_of_inverse)
if options.low_pass_filter:
from filter import filt_tanl
e1 =filt_tanl(e1,options.ff, options.aa)
e1.write_image(options.output)
elif options.window_stack:
nargs = len(args)
if nargs ==0:
print " Reduce image size of a stack"
return
else:
output_stack_name = None
inputstack = args[0]
if nargs ==2:output_stack_name = args[1]
input_path,input_file_name=os.path.split(inputstack)
input_file_name_root,ext=os.path.splitext(input_file_name)
if input_file_name_root[0:3]=="bdb":stack_is_bdb= True
else: stack_is_bdb= False
if output_stack_name is None:
if stack_is_bdb: output_stack_name ="bdb:reduced_"+input_file_name_root[4:]
else:output_stack_name = "reduced_"+input_file_name_root+".hdf" # Only hdf file is output.
nimage = EMUtil.get_image_count(inputstack)
from fundamentals import window2d
for i in xrange(nimage):
image = EMData()
image.read_image(inputstack,i)
w = window2d(image,options.box,options.box)
w.write_image(output_stack_name,i)
else: ERROR("Please provide option name","sxprocess.py",1)
def shiftali_MPI(stack,
maskfile=None,
maxit=100,
CTF=False,
snr=1.0,
Fourvar=False,
search_rng=-1,
oneDx=False,
search_rng_y=-1):
from applications import MPI_start_end
from utilities import model_circle, model_blank, get_image, peak_search, get_im
from utilities import reduce_EMData_to_root, bcast_EMData_to_all, send_attr_dict, file_type, bcast_number_to_all, bcast_list_to_all
from pap_statistics import varf2d_MPI
from fundamentals import fft, ccf, rot_shift3D, rot_shift2D
from utilities import get_params2D, set_params2D
from utilities import print_msg, print_begin_msg, print_end_msg
import os
import sys
from mpi import mpi_init, mpi_comm_size, mpi_comm_rank, MPI_COMM_WORLD
from mpi import mpi_reduce, mpi_bcast, mpi_barrier, mpi_gatherv
from mpi import MPI_SUM, MPI_FLOAT, MPI_INT
from EMAN2 import Processor
from time import time
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
myid = mpi_comm_rank(MPI_COMM_WORLD)
main_node = 0
ftp = file_type(stack)
if myid == main_node:
print_begin_msg("shiftali_MPI")
max_iter = int(maxit)
if myid == main_node:
if ftp == "bdb":
from EMAN2db import db_open_dict
dummy = db_open_dict(stack, True)
nima = EMUtil.get_image_count(stack)
else:
nima = 0
nima = bcast_number_to_all(nima, source_node=main_node)
list_of_particles = list(range(nima))
image_start, image_end = MPI_start_end(nima, number_of_proc, myid)
list_of_particles = list_of_particles[image_start:image_end]
# read nx and ctf_app (if CTF) and broadcast to all nodes
if myid == main_node:
ima = EMData()
ima.read_image(stack, list_of_particles[0], True)
nx = ima.get_xsize()
ny = ima.get_ysize()
if CTF: ctf_app = ima.get_attr_default('ctf_applied', 2)
del ima
else:
nx = 0
ny = 0
if CTF: ctf_app = 0
nx = bcast_number_to_all(nx, source_node=main_node)
ny = bcast_number_to_all(ny, source_node=main_node)
if CTF:
ctf_app = bcast_number_to_all(ctf_app, source_node=main_node)
if ctf_app > 0:
ERROR("data cannot be ctf-applied", "shiftali_MPI", 1, myid)
if maskfile == None:
mrad = min(nx, ny)
mask = model_circle(mrad // 2 - 2, nx, ny)
else:
mask = get_im(maskfile)
if CTF:
from filter import filt_ctf
from morphology import ctf_img
ctf_abs_sum = EMData(nx, ny, 1, False)
ctf_2_sum = EMData(nx, ny, 1, False)
else:
ctf_2_sum = None
from global_def import CACHE_DISABLE
if CACHE_DISABLE:
data = EMData.read_images(stack, list_of_particles)
else:
for i in range(number_of_proc):
if myid == i:
data = EMData.read_images(stack, list_of_particles)
if ftp == "bdb": mpi_barrier(MPI_COMM_WORLD)
for im in range(len(data)):
data[im].set_attr('ID', list_of_particles[im])
st = Util.infomask(data[im], mask, False)
data[im] -= st[0]
if CTF:
ctf_params = data[im].get_attr("ctf")
ctfimg = ctf_img(nx, ctf_params, ny=ny)
Util.add_img2(ctf_2_sum, ctfimg)
Util.add_img_abs(ctf_abs_sum, ctfimg)
if CTF:
reduce_EMData_to_root(ctf_2_sum, myid, main_node)
reduce_EMData_to_root(ctf_abs_sum, myid, main_node)
else:
ctf_2_sum = None
if CTF:
if myid != main_node:
del ctf_2_sum
del ctf_abs_sum
else:
temp = EMData(nx, ny, 1, False)
for i in range(0, nx, 2):
for j in range(ny):
temp.set_value_at(i, j, snr)
Util.add_img(ctf_2_sum, temp)
del temp
total_iter = 0
# apply initial xform.align2d parameters stored in header
init_params = []
for im in range(len(data)):
t = data[im].get_attr('xform.align2d')
init_params.append(t)
p = t.get_params("2d")
data[im] = rot_shift2D(data[im],
p['alpha'],
sx=p['tx'],
sy=p['ty'],
mirror=p['mirror'],
scale=p['scale'])
# fourier transform all images, and apply ctf if CTF
for im in range(len(data)):
if CTF:
ctf_params = data[im].get_attr("ctf")
data[im] = filt_ctf(fft(data[im]), ctf_params)
else:
data[im] = fft(data[im])
sx_sum = 0
sy_sum = 0
sx_sum_total = 0
sy_sum_total = 0
shift_x = [0.0] * len(data)
shift_y = [0.0] * len(data)
ishift_x = [0.0] * len(data)
ishift_y = [0.0] * len(data)
for Iter in range(max_iter):
if myid == main_node:
start_time = time()
print_msg("Iteration #%4d\n" % (total_iter))
total_iter += 1
avg = EMData(nx, ny, 1, False)
for im in data:
Util.add_img(avg, im)
reduce_EMData_to_root(avg, myid, main_node)
if myid == main_node:
if CTF:
tavg = Util.divn_filter(avg, ctf_2_sum)
else:
tavg = Util.mult_scalar(avg, 1.0 / float(nima))
else:
tavg = EMData(nx, ny, 1, False)
if Fourvar:
bcast_EMData_to_all(tavg, myid, main_node)
vav, rvar = varf2d_MPI(myid, data, tavg, mask, "a", CTF)
if myid == main_node:
if Fourvar:
tavg = fft(Util.divn_img(fft(tavg), vav))
vav_r = Util.pack_complex_to_real(vav)
# normalize and mask tavg in real space
tavg = fft(tavg)
stat = Util.infomask(tavg, mask, False)
tavg -= stat[0]
Util.mul_img(tavg, mask)
# For testing purposes: shift tavg to some random place and see if the centering is still correct
#tavg = rot_shift3D(tavg,sx=3,sy=-4)
tavg = fft(tavg)
if Fourvar: del vav
bcast_EMData_to_all(tavg, myid, main_node)
sx_sum = 0
sy_sum = 0
if search_rng > 0: nwx = 2 * search_rng + 1
else: nwx = nx
if search_rng_y > 0: nwy = 2 * search_rng_y + 1
else: nwy = ny
not_zero = 0
for im in range(len(data)):
if oneDx:
ctx = Util.window(ccf(data[im], tavg), nwx, 1)
p1 = peak_search(ctx)
p1_x = -int(p1[0][3])
ishift_x[im] = p1_x
sx_sum += p1_x
else:
p1 = peak_search(Util.window(ccf(data[im], tavg), nwx, nwy))
p1_x = -int(p1[0][4])
p1_y = -int(p1[0][5])
ishift_x[im] = p1_x
ishift_y[im] = p1_y
sx_sum += p1_x
sy_sum += p1_y
if not_zero == 0:
if (not (ishift_x[im] == 0.0)) or (not (ishift_y[im] == 0.0)):
not_zero = 1
sx_sum = mpi_reduce(sx_sum, 1, MPI_INT, MPI_SUM, main_node,
MPI_COMM_WORLD)
if not oneDx:
sy_sum = mpi_reduce(sy_sum, 1, MPI_INT, MPI_SUM, main_node,
MPI_COMM_WORLD)
if myid == main_node:
sx_sum_total = int(sx_sum[0])
if not oneDx:
sy_sum_total = int(sy_sum[0])
else:
sx_sum_total = 0
sy_sum_total = 0
sx_sum_total = bcast_number_to_all(sx_sum_total, source_node=main_node)
if not oneDx:
sy_sum_total = bcast_number_to_all(sy_sum_total,
source_node=main_node)
sx_ave = round(float(sx_sum_total) / nima)
sy_ave = round(float(sy_sum_total) / nima)
for im in range(len(data)):
p1_x = ishift_x[im] - sx_ave
p1_y = ishift_y[im] - sy_ave
params2 = {
"filter_type": Processor.fourier_filter_types.SHIFT,
"x_shift": p1_x,
"y_shift": p1_y,
"z_shift": 0.0
}
data[im] = Processor.EMFourierFilter(data[im], params2)
shift_x[im] += p1_x
shift_y[im] += p1_y
# stop if all shifts are zero
not_zero = mpi_reduce(not_zero, 1, MPI_INT, MPI_SUM, main_node,
MPI_COMM_WORLD)
if myid == main_node:
not_zero_all = int(not_zero[0])
else:
not_zero_all = 0
not_zero_all = bcast_number_to_all(not_zero_all, source_node=main_node)
if myid == main_node:
print_msg("Time of iteration = %12.2f\n" % (time() - start_time))
start_time = time()
if not_zero_all == 0: break
#for im in xrange(len(data)): data[im] = fft(data[im]) This should not be required as only header information is used
# combine shifts found with the original parameters
for im in range(len(data)):
t0 = init_params[im]
t1 = Transform()
t1.set_params({
"type": "2D",
"alpha": 0,
"scale": t0.get_scale(),
"mirror": 0,
"tx": shift_x[im],
"ty": shift_y[im]
})
# combine t0 and t1
tt = t1 * t0
data[im].set_attr("xform.align2d", tt)
# write out headers and STOP, under MPI writing has to be done sequentially
mpi_barrier(MPI_COMM_WORLD)
par_str = ["xform.align2d", "ID"]
if myid == main_node:
from utilities import file_type
if (file_type(stack) == "bdb"):
from utilities import recv_attr_dict_bdb
recv_attr_dict_bdb(main_node, stack, data, par_str, image_start,
image_end, number_of_proc)
else:
from utilities import recv_attr_dict
recv_attr_dict(main_node, stack, data, par_str, image_start,
image_end, number_of_proc)
else:
send_attr_dict(main_node, data, par_str, image_start, image_end)
if myid == main_node: print_end_msg("shiftali_MPI")
def shiftali_MPI(stack, maskfile=None, maxit=100, CTF=False, snr=1.0, Fourvar=False, search_rng=-1, oneDx=False, search_rng_y=-1):
from applications import MPI_start_end
from utilities import model_circle, model_blank, get_image, peak_search, get_im
from utilities import reduce_EMData_to_root, bcast_EMData_to_all, send_attr_dict, file_type, bcast_number_to_all, bcast_list_to_all
from statistics import varf2d_MPI
from fundamentals import fft, ccf, rot_shift3D, rot_shift2D
from utilities import get_params2D, set_params2D
from utilities import print_msg, print_begin_msg, print_end_msg
import os
import sys
from mpi import mpi_init, mpi_comm_size, mpi_comm_rank, MPI_COMM_WORLD
from mpi import mpi_reduce, mpi_bcast, mpi_barrier, mpi_gatherv
from mpi import MPI_SUM, MPI_FLOAT, MPI_INT
from EMAN2 import Processor
from time import time
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
myid = mpi_comm_rank(MPI_COMM_WORLD)
main_node = 0
ftp = file_type(stack)
if myid == main_node:
print_begin_msg("shiftali_MPI")
max_iter=int(maxit)
if myid == main_node:
if ftp == "bdb":
from EMAN2db import db_open_dict
dummy = db_open_dict(stack, True)
nima = EMUtil.get_image_count(stack)
else:
nima = 0
nima = bcast_number_to_all(nima, source_node = main_node)
list_of_particles = range(nima)
image_start, image_end = MPI_start_end(nima, number_of_proc, myid)
list_of_particles = list_of_particles[image_start: image_end]
# read nx and ctf_app (if CTF) and broadcast to all nodes
if myid == main_node:
ima = EMData()
ima.read_image(stack, list_of_particles[0], True)
nx = ima.get_xsize()
ny = ima.get_ysize()
if CTF: ctf_app = ima.get_attr_default('ctf_applied', 2)
del ima
else:
nx = 0
ny = 0
if CTF: ctf_app = 0
nx = bcast_number_to_all(nx, source_node = main_node)
ny = bcast_number_to_all(ny, source_node = main_node)
if CTF:
ctf_app = bcast_number_to_all(ctf_app, source_node = main_node)
if ctf_app > 0: ERROR("data cannot be ctf-applied", "shiftali_MPI", 1, myid)
if maskfile == None:
mrad = min(nx, ny)
mask = model_circle(mrad//2-2, nx, ny)
else:
mask = get_im(maskfile)
if CTF:
from filter import filt_ctf
from morphology import ctf_img
ctf_abs_sum = EMData(nx, ny, 1, False)
ctf_2_sum = EMData(nx, ny, 1, False)
else:
ctf_2_sum = None
from global_def import CACHE_DISABLE
if CACHE_DISABLE:
data = EMData.read_images(stack, list_of_particles)
else:
for i in xrange(number_of_proc):
if myid == i:
data = EMData.read_images(stack, list_of_particles)
if ftp == "bdb": mpi_barrier(MPI_COMM_WORLD)
for im in xrange(len(data)):
data[im].set_attr('ID', list_of_particles[im])
st = Util.infomask(data[im], mask, False)
data[im] -= st[0]
if CTF:
ctf_params = data[im].get_attr("ctf")
ctfimg = ctf_img(nx, ctf_params, ny=ny)
Util.add_img2(ctf_2_sum, ctfimg)
Util.add_img_abs(ctf_abs_sum, ctfimg)
if CTF:
reduce_EMData_to_root(ctf_2_sum, myid, main_node)
reduce_EMData_to_root(ctf_abs_sum, myid, main_node)
else: ctf_2_sum = None
if CTF:
if myid != main_node:
del ctf_2_sum
del ctf_abs_sum
else:
temp = EMData(nx, ny, 1, False)
for i in xrange(0,nx,2):
for j in xrange(ny):
temp.set_value_at(i,j,snr)
Util.add_img(ctf_2_sum, temp)
del temp
total_iter = 0
# apply initial xform.align2d parameters stored in header
init_params = []
for im in xrange(len(data)):
t = data[im].get_attr('xform.align2d')
init_params.append(t)
p = t.get_params("2d")
data[im] = rot_shift2D(data[im], p['alpha'], sx=p['tx'], sy=p['ty'], mirror=p['mirror'], scale=p['scale'])
# fourier transform all images, and apply ctf if CTF
for im in xrange(len(data)):
if CTF:
ctf_params = data[im].get_attr("ctf")
data[im] = filt_ctf(fft(data[im]), ctf_params)
else:
data[im] = fft(data[im])
sx_sum=0
sy_sum=0
sx_sum_total=0
sy_sum_total=0
shift_x = [0.0]*len(data)
shift_y = [0.0]*len(data)
ishift_x = [0.0]*len(data)
ishift_y = [0.0]*len(data)
for Iter in xrange(max_iter):
if myid == main_node:
start_time = time()
print_msg("Iteration #%4d\n"%(total_iter))
total_iter += 1
avg = EMData(nx, ny, 1, False)
for im in data: Util.add_img(avg, im)
reduce_EMData_to_root(avg, myid, main_node)
if myid == main_node:
if CTF:
tavg = Util.divn_filter(avg, ctf_2_sum)
else: tavg = Util.mult_scalar(avg, 1.0/float(nima))
else:
tavg = EMData(nx, ny, 1, False)
if Fourvar:
bcast_EMData_to_all(tavg, myid, main_node)
vav, rvar = varf2d_MPI(myid, data, tavg, mask, "a", CTF)
if myid == main_node:
if Fourvar:
tavg = fft(Util.divn_img(fft(tavg), vav))
vav_r = Util.pack_complex_to_real(vav)
# normalize and mask tavg in real space
tavg = fft(tavg)
stat = Util.infomask( tavg, mask, False )
tavg -= stat[0]
Util.mul_img(tavg, mask)
# For testing purposes: shift tavg to some random place and see if the centering is still correct
#tavg = rot_shift3D(tavg,sx=3,sy=-4)
tavg = fft(tavg)
if Fourvar: del vav
bcast_EMData_to_all(tavg, myid, main_node)
sx_sum=0
sy_sum=0
if search_rng > 0: nwx = 2*search_rng+1
else: nwx = nx
if search_rng_y > 0: nwy = 2*search_rng_y+1
else: nwy = ny
not_zero = 0
for im in xrange(len(data)):
if oneDx:
ctx = Util.window(ccf(data[im],tavg),nwx,1)
p1 = peak_search(ctx)
p1_x = -int(p1[0][3])
ishift_x[im] = p1_x
sx_sum += p1_x
else:
p1 = peak_search(Util.window(ccf(data[im],tavg), nwx,nwy))
p1_x = -int(p1[0][4])
p1_y = -int(p1[0][5])
ishift_x[im] = p1_x
ishift_y[im] = p1_y
sx_sum += p1_x
sy_sum += p1_y
if not_zero == 0:
if (not(ishift_x[im] == 0.0)) or (not(ishift_y[im] == 0.0)):
not_zero = 1
sx_sum = mpi_reduce(sx_sum, 1, MPI_INT, MPI_SUM, main_node, MPI_COMM_WORLD)
if not oneDx:
sy_sum = mpi_reduce(sy_sum, 1, MPI_INT, MPI_SUM, main_node, MPI_COMM_WORLD)
if myid == main_node:
sx_sum_total = int(sx_sum[0])
if not oneDx:
sy_sum_total = int(sy_sum[0])
else:
sx_sum_total = 0
sy_sum_total = 0
sx_sum_total = bcast_number_to_all(sx_sum_total, source_node = main_node)
if not oneDx:
sy_sum_total = bcast_number_to_all(sy_sum_total, source_node = main_node)
sx_ave = round(float(sx_sum_total)/nima)
sy_ave = round(float(sy_sum_total)/nima)
for im in xrange(len(data)):
p1_x = ishift_x[im] - sx_ave
p1_y = ishift_y[im] - sy_ave
params2 = {"filter_type" : Processor.fourier_filter_types.SHIFT, "x_shift" : p1_x, "y_shift" : p1_y, "z_shift" : 0.0}
data[im] = Processor.EMFourierFilter(data[im], params2)
shift_x[im] += p1_x
shift_y[im] += p1_y
# stop if all shifts are zero
not_zero = mpi_reduce(not_zero, 1, MPI_INT, MPI_SUM, main_node, MPI_COMM_WORLD)
if myid == main_node:
not_zero_all = int(not_zero[0])
else:
not_zero_all = 0
not_zero_all = bcast_number_to_all(not_zero_all, source_node = main_node)
if myid == main_node:
print_msg("Time of iteration = %12.2f\n"%(time()-start_time))
start_time = time()
if not_zero_all == 0: break
#for im in xrange(len(data)): data[im] = fft(data[im]) This should not be required as only header information is used
# combine shifts found with the original parameters
for im in xrange(len(data)):
t0 = init_params[im]
t1 = Transform()
t1.set_params({"type":"2D","alpha":0,"scale":t0.get_scale(),"mirror":0,"tx":shift_x[im],"ty":shift_y[im]})
# combine t0 and t1
tt = t1*t0
data[im].set_attr("xform.align2d", tt)
# write out headers and STOP, under MPI writing has to be done sequentially
mpi_barrier(MPI_COMM_WORLD)
par_str = ["xform.align2d", "ID"]
if myid == main_node:
from utilities import file_type
if(file_type(stack) == "bdb"):
from utilities import recv_attr_dict_bdb
recv_attr_dict_bdb(main_node, stack, data, par_str, image_start, image_end, number_of_proc)
else:
from utilities import recv_attr_dict
recv_attr_dict(main_node, stack, data, par_str, image_start, image_end, number_of_proc)
else: send_attr_dict(main_node, data, par_str, image_start, image_end)
if myid == main_node: print_end_msg("shiftali_MPI")
def helicalshiftali_MPI(stack,
maskfile=None,
maxit=100,
CTF=False,
snr=1.0,
Fourvar=False,
search_rng=-1):
from applications import MPI_start_end
from utilities import model_circle, model_blank, get_image, peak_search, get_im, pad
from utilities import reduce_EMData_to_root, bcast_EMData_to_all, send_attr_dict, file_type, bcast_number_to_all, bcast_list_to_all
from pap_statistics import varf2d_MPI
from fundamentals import fft, ccf, rot_shift3D, rot_shift2D, fshift
from utilities import get_params2D, set_params2D, chunks_distribution
from utilities import print_msg, print_begin_msg, print_end_msg
import os
import sys
from mpi import mpi_init, mpi_comm_size, mpi_comm_rank, MPI_COMM_WORLD
from mpi import mpi_reduce, mpi_bcast, mpi_barrier, mpi_gatherv
from mpi import MPI_SUM, MPI_FLOAT, MPI_INT
from time import time
from pixel_error import ordersegments
from math import sqrt, atan2, tan, pi
nproc = mpi_comm_size(MPI_COMM_WORLD)
myid = mpi_comm_rank(MPI_COMM_WORLD)
main_node = 0
ftp = file_type(stack)
if myid == main_node:
print_begin_msg("helical-shiftali_MPI")
max_iter = int(maxit)
if (myid == main_node):
infils = EMUtil.get_all_attributes(stack, "filament")
ptlcoords = EMUtil.get_all_attributes(stack, 'ptcl_source_coord')
filaments = ordersegments(infils, ptlcoords)
total_nfils = len(filaments)
inidl = [0] * total_nfils
for i in range(total_nfils):
inidl[i] = len(filaments[i])
linidl = sum(inidl)
nima = linidl
tfilaments = []
for i in range(total_nfils):
tfilaments += filaments[i]
del filaments
else:
total_nfils = 0
linidl = 0
total_nfils = bcast_number_to_all(total_nfils, source_node=main_node)
if myid != main_node:
inidl = [-1] * total_nfils
inidl = bcast_list_to_all(inidl, myid, source_node=main_node)
linidl = bcast_number_to_all(linidl, source_node=main_node)
if myid != main_node:
tfilaments = [-1] * linidl
tfilaments = bcast_list_to_all(tfilaments, myid, source_node=main_node)
filaments = []
iendi = 0
for i in range(total_nfils):
isti = iendi
iendi = isti + inidl[i]
filaments.append(tfilaments[isti:iendi])
del tfilaments, inidl
if myid == main_node:
print_msg("total number of filaments: %d" % total_nfils)
if total_nfils < nproc:
ERROR(
'number of CPUs (%i) is larger than the number of filaments (%i), please reduce the number of CPUs used'
% (nproc, total_nfils), "ehelix_MPI", 1, myid)
# balanced load
temp = chunks_distribution([[len(filaments[i]), i]
for i in range(len(filaments))],
nproc)[myid:myid + 1][0]
filaments = [filaments[temp[i][1]] for i in range(len(temp))]
nfils = len(filaments)
#filaments = [[0,1]]
#print "filaments",filaments
list_of_particles = []
indcs = []
k = 0
for i in range(nfils):
list_of_particles += filaments[i]
k1 = k + len(filaments[i])
indcs.append([k, k1])
k = k1
data = EMData.read_images(stack, list_of_particles)
ldata = len(data)
print("ldata=", ldata)
nx = data[0].get_xsize()
ny = data[0].get_ysize()
if maskfile == None:
mrad = min(nx, ny) // 2 - 2
mask = pad(model_blank(2 * mrad + 1, ny, 1, 1.0), nx, ny, 1, 0.0)
else:
mask = get_im(maskfile)
# apply initial xform.align2d parameters stored in header
init_params = []
for im in range(ldata):
t = data[im].get_attr('xform.align2d')
init_params.append(t)
p = t.get_params("2d")
data[im] = rot_shift2D(data[im], p['alpha'], p['tx'], p['ty'],
p['mirror'], p['scale'])
if CTF:
from filter import filt_ctf
from morphology import ctf_img
ctf_abs_sum = EMData(nx, ny, 1, False)
ctf_2_sum = EMData(nx, ny, 1, False)
else:
ctf_2_sum = None
ctf_abs_sum = None
from utilities import info
for im in range(ldata):
data[im].set_attr('ID', list_of_particles[im])
st = Util.infomask(data[im], mask, False)
data[im] -= st[0]
if CTF:
ctf_params = data[im].get_attr("ctf")
qctf = data[im].get_attr("ctf_applied")
if qctf == 0:
data[im] = filt_ctf(fft(data[im]), ctf_params)
data[im].set_attr('ctf_applied', 1)
elif qctf != 1:
ERROR('Incorrectly set qctf flag', "helicalshiftali_MPI", 1,
myid)
ctfimg = ctf_img(nx, ctf_params, ny=ny)
Util.add_img2(ctf_2_sum, ctfimg)
Util.add_img_abs(ctf_abs_sum, ctfimg)
else:
data[im] = fft(data[im])
del list_of_particles
if CTF:
reduce_EMData_to_root(ctf_2_sum, myid, main_node)
reduce_EMData_to_root(ctf_abs_sum, myid, main_node)
if CTF:
if myid != main_node:
del ctf_2_sum
del ctf_abs_sum
else:
temp = EMData(nx, ny, 1, False)
tsnr = 1. / snr
for i in range(0, nx + 2, 2):
for j in range(ny):
temp.set_value_at(i, j, tsnr)
temp.set_value_at(i + 1, j, 0.0)
#info(ctf_2_sum)
Util.add_img(ctf_2_sum, temp)
#info(ctf_2_sum)
del temp
total_iter = 0
shift_x = [0.0] * ldata
for Iter in range(max_iter):
if myid == main_node:
start_time = time()
print_msg("Iteration #%4d\n" % (total_iter))
total_iter += 1
avg = EMData(nx, ny, 1, False)
for im in range(ldata):
Util.add_img(avg, fshift(data[im], shift_x[im]))
reduce_EMData_to_root(avg, myid, main_node)
if myid == main_node:
if CTF: tavg = Util.divn_filter(avg, ctf_2_sum)
else: tavg = Util.mult_scalar(avg, 1.0 / float(nima))
else:
tavg = model_blank(nx, ny)
if Fourvar:
bcast_EMData_to_all(tavg, myid, main_node)
vav, rvar = varf2d_MPI(myid, data, tavg, mask, "a", CTF)
if myid == main_node:
if Fourvar:
tavg = fft(Util.divn_img(fft(tavg), vav))
vav_r = Util.pack_complex_to_real(vav)
# normalize and mask tavg in real space
tavg = fft(tavg)
stat = Util.infomask(tavg, mask, False)
tavg -= stat[0]
Util.mul_img(tavg, mask)
tavg.write_image("tavg.hdf", Iter)
# For testing purposes: shift tavg to some random place and see if the centering is still correct
#tavg = rot_shift3D(tavg,sx=3,sy=-4)
if Fourvar: del vav
bcast_EMData_to_all(tavg, myid, main_node)
tavg = fft(tavg)
sx_sum = 0.0
nxc = nx // 2
for ifil in range(nfils):
"""
# Calculate filament average
avg = EMData(nx, ny, 1, False)
filnima = 0
for im in xrange(indcs[ifil][0], indcs[ifil][1]):
Util.add_img(avg, data[im])
filnima += 1
tavg = Util.mult_scalar(avg, 1.0/float(filnima))
"""
# Calculate 1D ccf between each segment and filament average
nsegms = indcs[ifil][1] - indcs[ifil][0]
ctx = [None] * nsegms
pcoords = [None] * nsegms
for im in range(indcs[ifil][0], indcs[ifil][1]):
ctx[im - indcs[ifil][0]] = Util.window(ccf(tavg, data[im]), nx,
1)
pcoords[im - indcs[ifil][0]] = data[im].get_attr(
'ptcl_source_coord')
#ctx[im-indcs[ifil][0]].write_image("ctx.hdf",im-indcs[ifil][0])
#print " CTX ",myid,im,Util.infomask(ctx[im-indcs[ifil][0]], None, True)
# search for best x-shift
cents = nsegms // 2
dst = sqrt(
max((pcoords[cents][0] - pcoords[0][0])**2 +
(pcoords[cents][1] - pcoords[0][1])**2,
(pcoords[cents][0] - pcoords[-1][0])**2 +
(pcoords[cents][1] - pcoords[-1][1])**2))
maxincline = atan2(ny // 2 - 2 - float(search_rng), dst)
kang = int(dst * tan(maxincline) + 0.5)
#print " settings ",nsegms,cents,dst,search_rng,maxincline,kang
# ## C code for alignment. @ming
results = [0.0] * 3
results = Util.helixshiftali(ctx, pcoords, nsegms, maxincline,
kang, search_rng, nxc)
sib = int(results[0])
bang = results[1]
qm = results[2]
#print qm, sib, bang
# qm = -1.e23
#
# for six in xrange(-search_rng, search_rng+1,1):
# q0 = ctx[cents].get_value_at(six+nxc)
# for incline in xrange(kang+1):
# qt = q0
# qu = q0
# if(kang>0): tang = tan(maxincline/kang*incline)
# else: tang = 0.0
# for kim in xrange(cents+1,nsegms):
# dst = sqrt((pcoords[cents][0] - pcoords[kim][0])**2 + (pcoords[cents][1] - pcoords[kim][1])**2)
# xl = dst*tang+six+nxc
# ixl = int(xl)
# dxl = xl - ixl
# #print " A ", ifil,six,incline,kim,xl,ixl,dxl
# qt += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# xl = -dst*tang+six+nxc
# ixl = int(xl)
# dxl = xl - ixl
# qu += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# for kim in xrange(cents):
# dst = sqrt((pcoords[cents][0] - pcoords[kim][0])**2 + (pcoords[cents][1] - pcoords[kim][1])**2)
# xl = -dst*tang+six+nxc
# ixl = int(xl)
# dxl = xl - ixl
# qt += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# xl = dst*tang+six+nxc
# ixl = int(xl)
# dxl = xl - ixl
# qu += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# if( qt > qm ):
# qm = qt
# sib = six
# bang = tang
# if( qu > qm ):
# qm = qu
# sib = six
# bang = -tang
#if incline == 0: print "incline = 0 ",six,tang,qt,qu
#print qm,six,sib,bang
#print " got results ",indcs[ifil][0], indcs[ifil][1], ifil,myid,qm,sib,tang,bang,len(ctx),Util.infomask(ctx[0], None, True)
for im in range(indcs[ifil][0], indcs[ifil][1]):
kim = im - indcs[ifil][0]
dst = sqrt((pcoords[cents][0] - pcoords[kim][0])**2 +
(pcoords[cents][1] - pcoords[kim][1])**2)
if (kim < cents): xl = -dst * bang + sib
else: xl = dst * bang + sib
shift_x[im] = xl
# Average shift
sx_sum += shift_x[indcs[ifil][0] + cents]
# #print myid,sx_sum,total_nfils
sx_sum = mpi_reduce(sx_sum, 1, MPI_FLOAT, MPI_SUM, main_node,
MPI_COMM_WORLD)
if myid == main_node:
sx_sum = float(sx_sum[0]) / total_nfils
print_msg("Average shift %6.2f\n" % (sx_sum))
else:
sx_sum = 0.0
sx_sum = 0.0
sx_sum = bcast_number_to_all(sx_sum, source_node=main_node)
for im in range(ldata):
shift_x[im] -= sx_sum
#print " %3d %6.3f"%(im,shift_x[im])
#exit()
# combine shifts found with the original parameters
for im in range(ldata):
t1 = Transform()
##import random
##shix=random.randint(-10, 10)
##t1.set_params({"type":"2D","tx":shix})
t1.set_params({"type": "2D", "tx": shift_x[im]})
# combine t0 and t1
tt = t1 * init_params[im]
data[im].set_attr("xform.align2d", tt)
# write out headers and STOP, under MPI writing has to be done sequentially
mpi_barrier(MPI_COMM_WORLD)
par_str = ["xform.align2d", "ID"]
if myid == main_node:
from utilities import file_type
if (file_type(stack) == "bdb"):
from utilities import recv_attr_dict_bdb
recv_attr_dict_bdb(main_node, stack, data, par_str, 0, ldata,
nproc)
else:
from utilities import recv_attr_dict
recv_attr_dict(main_node, stack, data, par_str, 0, ldata, nproc)
else:
send_attr_dict(main_node, data, par_str, 0, ldata)
if myid == main_node: print_end_msg("helical-shiftali_MPI")
def main():
from optparse import OptionParser
from global_def import SPARXVERSION
from EMAN2 import EMData
from logger import Logger, BaseLogger_Files
import sys, os, time
global Tracker, Blockdata
from global_def import ERROR
progname = os.path.basename(sys.argv[0])
usage = progname + " --output_dir=output_dir --isac_dir=output_dir_of_isac "
parser = OptionParser(usage, version=SPARXVERSION)
parser.add_option("--pw_adjustment", type ="string", default ='analytical_model', \
help="adjust power spectrum of 2-D averages to an analytic model. Other opions: no_adjustment; bfactor; a text file of 1D rotationally averaged PW")
#### Four options for --pw_adjustment:
# 1> analytical_model(default);
# 2> no_adjustment;
# 3> bfactor;
# 4> adjust_to_given_pw2(user has to provide a text file that contains 1D rotationally averaged PW)
# options in common
parser.add_option(
"--isac_dir",
type="string",
default='',
help="ISAC run output directory, input directory for this command")
parser.add_option(
"--output_dir",
type="string",
default='',
help="output directory where computed averages are saved")
parser.add_option(
"--pixel_size",
type="float",
default=-1.0,
help=
"pixel_size of raw images. one can put 1.0 in case of negative stain data"
)
parser.add_option(
"--fl",
type="float",
default=-1.0,
help=
"low pass filter, = -1.0, not applied; =0.0, using FH1 (initial resolution), = 1.0 using FH2 (resolution after local alignment), or user provided value in absolute freqency [0.0:0.5]"
)
parser.add_option("--stack",
type="string",
default="",
help="data stack used in ISAC")
parser.add_option("--radius", type="int", default=-1, help="radius")
parser.add_option("--xr",
type="float",
default=-1.0,
help="local alignment search range")
#parser.add_option("--ts", type ="float", default =1.0, help= "local alignment search step")
parser.add_option("--fh",
type="float",
default=-1.0,
help="local alignment high frequencies limit")
#parser.add_option("--maxit", type ="int", default =5, help= "local alignment iterations")
parser.add_option("--navg",
type="int",
default=1000000,
help="number of aveages")
parser.add_option("--local_alignment",
action="store_true",
default=False,
help="do local alignment")
parser.add_option(
"--noctf",
action="store_true",
default=False,
help=
"no ctf correction, useful for negative stained data. always ctf for cryo data"
)
parser.add_option(
"--B_start",
type="float",
default=45.0,
help=
"start frequency (Angstrom) of power spectrum for B_factor estimation")
parser.add_option(
"--Bfactor",
type="float",
default=-1.0,
help=
"User defined bactors (e.g. 25.0[A^2]). By default, the program automatically estimates B-factor. "
)
(options, args) = parser.parse_args(sys.argv[1:])
adjust_to_analytic_model = False
adjust_to_given_pw2 = False
B_enhance = False
no_adjustment = False
if options.pw_adjustment == 'analytical_model':
adjust_to_analytic_model = True
elif options.pw_adjustment == 'no_adjustment':
no_adjustment = True
elif options.pw_adjustment == 'bfactor':
B_enhance = True
else:
adjust_to_given_pw2 = True
from utilities import get_im, bcast_number_to_all, write_text_file, read_text_file, wrap_mpi_bcast, write_text_row
from utilities import cmdexecute
from filter import filt_tanl
from logger import Logger, BaseLogger_Files
import user_functions
import string
from string import split, atoi, atof
import json
mpi_init(0, [])
nproc = mpi_comm_size(MPI_COMM_WORLD)
myid = mpi_comm_rank(MPI_COMM_WORLD)
Blockdata = {}
# MPI stuff
Blockdata["nproc"] = nproc
Blockdata["myid"] = myid
Blockdata["main_node"] = 0
Blockdata["shared_comm"] = mpi_comm_split_type(MPI_COMM_WORLD,
MPI_COMM_TYPE_SHARED, 0,
MPI_INFO_NULL)
Blockdata["myid_on_node"] = mpi_comm_rank(Blockdata["shared_comm"])
Blockdata["no_of_processes_per_group"] = mpi_comm_size(
Blockdata["shared_comm"])
masters_from_groups_vs_everything_else_comm = mpi_comm_split(
MPI_COMM_WORLD, Blockdata["main_node"] == Blockdata["myid_on_node"],
Blockdata["myid_on_node"])
Blockdata["color"], Blockdata["no_of_groups"], balanced_processor_load_on_nodes = get_colors_and_subsets(Blockdata["main_node"], MPI_COMM_WORLD, Blockdata["myid"], \
Blockdata["shared_comm"], Blockdata["myid_on_node"], masters_from_groups_vs_everything_else_comm)
# We need two nodes for processing of volumes
Blockdata["node_volume"] = [
Blockdata["no_of_groups"] - 3, Blockdata["no_of_groups"] - 2,
Blockdata["no_of_groups"] - 1
] # For 3D stuff take three last nodes
# We need two CPUs for processing of volumes, they are taken to be main CPUs on each volume
# We have to send the two myids to all nodes so we can identify main nodes on two selected groups.
Blockdata["nodes"] = [Blockdata["node_volume"][0]*Blockdata["no_of_processes_per_group"],Blockdata["node_volume"][1]*Blockdata["no_of_processes_per_group"], \
Blockdata["node_volume"][2]*Blockdata["no_of_processes_per_group"]]
# End of Blockdata: sorting requires at least three nodes, and the used number of nodes be integer times of three
global_def.BATCH = True
global_def.MPI = True
if adjust_to_given_pw2:
checking_flag = 0
if (Blockdata["myid"] == Blockdata["main_node"]):
if not os.path.exists(options.pw_adjustment): checking_flag = 1
checking_flag = bcast_number_to_all(checking_flag,
Blockdata["main_node"],
MPI_COMM_WORLD)
if checking_flag == 1:
ERROR("User provided power spectrum does not exist",
"sxcompute_isac_avg.py", 1, Blockdata["myid"])
Tracker = {}
Constants = {}
Constants["isac_dir"] = options.isac_dir
Constants["masterdir"] = options.output_dir
Constants["pixel_size"] = options.pixel_size
Constants["orgstack"] = options.stack
Constants["radius"] = options.radius
Constants["xrange"] = options.xr
Constants["FH"] = options.fh
Constants["low_pass_filter"] = options.fl
#Constants["maxit"] = options.maxit
Constants["navg"] = options.navg
Constants["B_start"] = options.B_start
Constants["Bfactor"] = options.Bfactor
if adjust_to_given_pw2: Constants["modelpw"] = options.pw_adjustment
Tracker["constants"] = Constants
# -------------------------------------------------------------
#
# Create and initialize Tracker dictionary with input options # State Variables
#<<<---------------------->>>imported functions<<<---------------------------------------------
#x_range = max(Tracker["constants"]["xrange"], int(1./Tracker["ini_shrink"])+1)
#y_range = x_range
####-----------------------------------------------------------
# Create Master directory and associated subdirectories
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
if Tracker["constants"]["masterdir"] == Tracker["constants"]["isac_dir"]:
masterdir = os.path.join(Tracker["constants"]["isac_dir"], "sharpen")
else:
masterdir = Tracker["constants"]["masterdir"]
if (Blockdata["myid"] == Blockdata["main_node"]):
msg = "Postprocessing ISAC 2D averages starts"
print(line, "Postprocessing ISAC 2D averages starts")
if not masterdir:
timestring = strftime("_%d_%b_%Y_%H_%M_%S", localtime())
masterdir = "sharpen_" + Tracker["constants"]["isac_dir"]
os.mkdir(masterdir)
else:
if os.path.exists(masterdir):
print("%s already exists" % masterdir)
else:
os.mkdir(masterdir)
subdir_path = os.path.join(masterdir, "ali2d_local_params_avg")
if not os.path.exists(subdir_path): os.mkdir(subdir_path)
subdir_path = os.path.join(masterdir, "params_avg")
if not os.path.exists(subdir_path): os.mkdir(subdir_path)
li = len(masterdir)
else:
li = 0
li = mpi_bcast(li, 1, MPI_INT, Blockdata["main_node"], MPI_COMM_WORLD)[0]
masterdir = mpi_bcast(masterdir, li, MPI_CHAR, Blockdata["main_node"],
MPI_COMM_WORLD)
masterdir = string.join(masterdir, "")
Tracker["constants"]["masterdir"] = masterdir
log_main = Logger(BaseLogger_Files())
log_main.prefix = Tracker["constants"]["masterdir"] + "/"
while not os.path.exists(Tracker["constants"]["masterdir"]):
print("Node ", Blockdata["myid"], " waiting...",
Tracker["constants"]["masterdir"])
sleep(1)
mpi_barrier(MPI_COMM_WORLD)
if (Blockdata["myid"] == Blockdata["main_node"]):
init_dict = {}
print(Tracker["constants"]["isac_dir"])
Tracker["directory"] = os.path.join(Tracker["constants"]["isac_dir"],
"2dalignment")
core = read_text_row(
os.path.join(Tracker["directory"], "initial2Dparams.txt"))
for im in range(len(core)):
init_dict[im] = core[im]
del core
else:
init_dict = 0
init_dict = wrap_mpi_bcast(init_dict,
Blockdata["main_node"],
communicator=MPI_COMM_WORLD)
###
do_ctf = True
if options.noctf: do_ctf = False
if (Blockdata["myid"] == Blockdata["main_node"]):
if do_ctf: print("CTF correction is on")
else: print("CTF correction is off")
if options.local_alignment: print("local refinement is on")
else: print("local refinement is off")
if B_enhance: print("Bfactor is to be applied on averages")
elif adjust_to_given_pw2:
print("PW of averages is adjusted to a given 1D PW curve")
elif adjust_to_analytic_model:
print("PW of averages is adjusted to analytical model")
else:
print("PW of averages is not adjusted")
#Tracker["constants"]["orgstack"] = "bdb:"+ os.path.join(Tracker["constants"]["isac_dir"],"../","sparx_stack")
image = get_im(Tracker["constants"]["orgstack"], 0)
Tracker["constants"]["nnxo"] = image.get_xsize()
if Tracker["constants"]["pixel_size"] == -1.0:
print(
"Pixel size value is not provided by user. extracting it from ctf header entry of the original stack."
)
try:
ctf_params = image.get_attr("ctf")
Tracker["constants"]["pixel_size"] = ctf_params.apix
except:
ERROR(
"Pixel size could not be extracted from the original stack.",
"sxcompute_isac_avg.py", 1,
Blockdata["myid"]) # action=1 - fatal error, exit
## Now fill in low-pass filter
isac_shrink_path = os.path.join(Tracker["constants"]["isac_dir"],
"README_shrink_ratio.txt")
if not os.path.exists(isac_shrink_path):
ERROR(
"%s does not exist in the specified ISAC run output directory"
% (isac_shrink_path), "sxcompute_isac_avg.py", 1,
Blockdata["myid"]) # action=1 - fatal error, exit
isac_shrink_file = open(isac_shrink_path, "r")
isac_shrink_lines = isac_shrink_file.readlines()
isac_shrink_ratio = float(
isac_shrink_lines[5]
) # 6th line: shrink ratio (= [target particle radius]/[particle radius]) used in the ISAC run
isac_radius = float(
isac_shrink_lines[6]
) # 7th line: particle radius at original pixel size used in the ISAC run
isac_shrink_file.close()
print("Extracted parameter values")
print("ISAC shrink ratio : {0}".format(isac_shrink_ratio))
print("ISAC particle radius : {0}".format(isac_radius))
Tracker["ini_shrink"] = isac_shrink_ratio
else:
Tracker["ini_shrink"] = 0.0
Tracker = wrap_mpi_bcast(Tracker,
Blockdata["main_node"],
communicator=MPI_COMM_WORLD)
#print(Tracker["constants"]["pixel_size"], "pixel_size")
x_range = max(Tracker["constants"]["xrange"],
int(1. / Tracker["ini_shrink"] + 0.99999))
a_range = y_range = x_range
if (Blockdata["myid"] == Blockdata["main_node"]):
parameters = read_text_row(
os.path.join(Tracker["constants"]["isac_dir"],
"all_parameters.txt"))
else:
parameters = 0
parameters = wrap_mpi_bcast(parameters,
Blockdata["main_node"],
communicator=MPI_COMM_WORLD)
params_dict = {}
list_dict = {}
#parepare params_dict
#navg = min(Tracker["constants"]["navg"]*Blockdata["nproc"], EMUtil.get_image_count(os.path.join(Tracker["constants"]["isac_dir"], "class_averages.hdf")))
navg = min(
Tracker["constants"]["navg"],
EMUtil.get_image_count(
os.path.join(Tracker["constants"]["isac_dir"],
"class_averages.hdf")))
global_dict = {}
ptl_list = []
memlist = []
if (Blockdata["myid"] == Blockdata["main_node"]):
print("Number of averages computed in this run is %d" % navg)
for iavg in range(navg):
params_of_this_average = []
image = get_im(
os.path.join(Tracker["constants"]["isac_dir"],
"class_averages.hdf"), iavg)
members = sorted(image.get_attr("members"))
memlist.append(members)
for im in range(len(members)):
abs_id = members[im]
global_dict[abs_id] = [iavg, im]
P = combine_params2( init_dict[abs_id][0], init_dict[abs_id][1], init_dict[abs_id][2], init_dict[abs_id][3], \
parameters[abs_id][0], parameters[abs_id][1]/Tracker["ini_shrink"], parameters[abs_id][2]/Tracker["ini_shrink"], parameters[abs_id][3])
if parameters[abs_id][3] == -1:
print(
"WARNING: Image #{0} is an unaccounted particle with invalid 2D alignment parameters and should not be the member of any classes. Please check the consitency of input dataset."
.format(abs_id)
) # How to check what is wrong about mirror = -1 (Toshio 2018/01/11)
params_of_this_average.append([P[0], P[1], P[2], P[3], 1.0])
ptl_list.append(abs_id)
params_dict[iavg] = params_of_this_average
list_dict[iavg] = members
write_text_row(
params_of_this_average,
os.path.join(Tracker["constants"]["masterdir"], "params_avg",
"params_avg_%03d.txt" % iavg))
ptl_list.sort()
init_params = [None for im in range(len(ptl_list))]
for im in range(len(ptl_list)):
init_params[im] = [ptl_list[im]] + params_dict[global_dict[
ptl_list[im]][0]][global_dict[ptl_list[im]][1]]
write_text_row(
init_params,
os.path.join(Tracker["constants"]["masterdir"],
"init_isac_params.txt"))
else:
params_dict = 0
list_dict = 0
memlist = 0
params_dict = wrap_mpi_bcast(params_dict,
Blockdata["main_node"],
communicator=MPI_COMM_WORLD)
list_dict = wrap_mpi_bcast(list_dict,
Blockdata["main_node"],
communicator=MPI_COMM_WORLD)
memlist = wrap_mpi_bcast(memlist,
Blockdata["main_node"],
communicator=MPI_COMM_WORLD)
# Now computing!
del init_dict
tag_sharpen_avg = 1000
## always apply low pass filter to B_enhanced images to suppress noise in high frequencies
enforced_to_H1 = False
if B_enhance:
if Tracker["constants"]["low_pass_filter"] == -1.0:
enforced_to_H1 = True
if navg < Blockdata["nproc"]: # Each CPU do one average
ERROR("number of nproc is larger than number of averages",
"sxcompute_isac_avg.py", 1, Blockdata["myid"])
else:
FH_list = [[0, 0.0, 0.0] for im in range(navg)]
image_start, image_end = MPI_start_end(navg, Blockdata["nproc"],
Blockdata["myid"])
if Blockdata["myid"] == Blockdata["main_node"]:
cpu_dict = {}
for iproc in range(Blockdata["nproc"]):
local_image_start, local_image_end = MPI_start_end(
navg, Blockdata["nproc"], iproc)
for im in range(local_image_start, local_image_end):
cpu_dict[im] = iproc
else:
cpu_dict = 0
cpu_dict = wrap_mpi_bcast(cpu_dict,
Blockdata["main_node"],
communicator=MPI_COMM_WORLD)
slist = [None for im in range(navg)]
ini_list = [None for im in range(navg)]
avg1_list = [None for im in range(navg)]
avg2_list = [None for im in range(navg)]
plist_dict = {}
data_list = [None for im in range(navg)]
if Blockdata["myid"] == Blockdata["main_node"]:
if B_enhance:
print(
"Avg ID B-factor FH1(Res before ali) FH2(Res after ali)"
)
else:
print("Avg ID FH1(Res before ali) FH2(Res after ali)")
for iavg in range(image_start, image_end):
mlist = EMData.read_images(Tracker["constants"]["orgstack"],
list_dict[iavg])
for im in range(len(mlist)):
#mlist[im]= get_im(Tracker["constants"]["orgstack"], list_dict[iavg][im])
set_params2D(mlist[im],
params_dict[iavg][im],
xform="xform.align2d")
if options.local_alignment:
"""
new_average1 = within_group_refinement([mlist[kik] for kik in range(0,len(mlist),2)], maskfile= None, randomize= False, ir=1.0, \
ou=Tracker["constants"]["radius"], rs=1.0, xrng=[x_range], yrng=[y_range], step=[Tracker["constants"]["xstep"]], \
dst=0.0, maxit=Tracker["constants"]["maxit"], FH=max(Tracker["constants"]["FH"], FH1), FF=0.02, method="")
new_average2 = within_group_refinement([mlist[kik] for kik in range(1,len(mlist),2)], maskfile= None, randomize= False, ir=1.0, \
ou= Tracker["constants"]["radius"], rs=1.0, xrng=[ x_range], yrng=[y_range], step=[Tracker["constants"]["xstep"]], \
dst=0.0, maxit=Tracker["constants"]["maxit"], FH = max(Tracker["constants"]["FH"], FH1), FF=0.02, method="")
new_avg, frc, plist = compute_average(mlist, Tracker["constants"]["radius"], do_ctf)
"""
new_avg, plist, FH2 = refinement_2d_local(
mlist,
Tracker["constants"]["radius"],
a_range,
x_range,
y_range,
CTF=do_ctf,
SNR=1.0e10)
plist_dict[iavg] = plist
FH1 = -1.0
else:
new_avg, frc, plist = compute_average(
mlist, Tracker["constants"]["radius"], do_ctf)
FH1 = get_optimistic_res(frc)
FH2 = -1.0
#write_text_file(frc, os.path.join(Tracker["constants"]["masterdir"], "fsc%03d.txt"%iavg))
FH_list[iavg] = [iavg, FH1, FH2]
if B_enhance:
new_avg, gb = apply_enhancement(
new_avg, Tracker["constants"]["B_start"],
Tracker["constants"]["pixel_size"],
Tracker["constants"]["Bfactor"])
print(" %6d %6.3f %4.3f %4.3f" % (iavg, gb, FH1, FH2))
elif adjust_to_given_pw2:
roo = read_text_file(Tracker["constants"]["modelpw"], -1)
roo = roo[0] # always on the first column
new_avg = adjust_pw_to_model(
new_avg, Tracker["constants"]["pixel_size"], roo)
print(" %6d %4.3f %4.3f " % (iavg, FH1, FH2))
elif adjust_to_analytic_model:
new_avg = adjust_pw_to_model(
new_avg, Tracker["constants"]["pixel_size"], None)
print(" %6d %4.3f %4.3f " % (iavg, FH1, FH2))
elif no_adjustment:
pass
if Tracker["constants"]["low_pass_filter"] != -1.0:
if Tracker["constants"]["low_pass_filter"] == 0.0:
low_pass_filter = FH1
elif Tracker["constants"]["low_pass_filter"] == 1.0:
low_pass_filter = FH2
if not options.local_alignment: low_pass_filter = FH1
else:
low_pass_filter = Tracker["constants"]["low_pass_filter"]
if low_pass_filter >= 0.45: low_pass_filter = 0.45
new_avg = filt_tanl(new_avg, low_pass_filter, 0.02)
else: # No low pass filter but if enforced
if enforced_to_H1: new_avg = filt_tanl(new_avg, FH1, 0.02)
if B_enhance: new_avg = fft(new_avg)
new_avg.set_attr("members", list_dict[iavg])
new_avg.set_attr("n_objects", len(list_dict[iavg]))
slist[iavg] = new_avg
print(
strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>",
"Refined average %7d" % iavg)
## send to main node to write
mpi_barrier(MPI_COMM_WORLD)
for im in range(navg):
# avg
if cpu_dict[im] == Blockdata[
"myid"] and Blockdata["myid"] != Blockdata["main_node"]:
send_EMData(slist[im], Blockdata["main_node"], tag_sharpen_avg)
elif cpu_dict[im] == Blockdata["myid"] and Blockdata[
"myid"] == Blockdata["main_node"]:
slist[im].set_attr("members", memlist[im])
slist[im].set_attr("n_objects", len(memlist[im]))
slist[im].write_image(
os.path.join(Tracker["constants"]["masterdir"],
"class_averages.hdf"), im)
elif cpu_dict[im] != Blockdata["myid"] and Blockdata[
"myid"] == Blockdata["main_node"]:
new_avg_other_cpu = recv_EMData(cpu_dict[im], tag_sharpen_avg)
new_avg_other_cpu.set_attr("members", memlist[im])
new_avg_other_cpu.set_attr("n_objects", len(memlist[im]))
new_avg_other_cpu.write_image(
os.path.join(Tracker["constants"]["masterdir"],
"class_averages.hdf"), im)
if options.local_alignment:
if cpu_dict[im] == Blockdata["myid"]:
write_text_row(
plist_dict[im],
os.path.join(Tracker["constants"]["masterdir"],
"ali2d_local_params_avg",
"ali2d_local_params_avg_%03d.txt" % im))
if cpu_dict[im] == Blockdata[
"myid"] and cpu_dict[im] != Blockdata["main_node"]:
wrap_mpi_send(plist_dict[im], Blockdata["main_node"],
MPI_COMM_WORLD)
wrap_mpi_send(FH_list, Blockdata["main_node"],
MPI_COMM_WORLD)
elif cpu_dict[im] != Blockdata["main_node"] and Blockdata[
"myid"] == Blockdata["main_node"]:
dummy = wrap_mpi_recv(cpu_dict[im], MPI_COMM_WORLD)
plist_dict[im] = dummy
dummy = wrap_mpi_recv(cpu_dict[im], MPI_COMM_WORLD)
FH_list[im] = dummy[im]
else:
if cpu_dict[im] == Blockdata[
"myid"] and cpu_dict[im] != Blockdata["main_node"]:
wrap_mpi_send(FH_list, Blockdata["main_node"],
MPI_COMM_WORLD)
elif cpu_dict[im] != Blockdata["main_node"] and Blockdata[
"myid"] == Blockdata["main_node"]:
dummy = wrap_mpi_recv(cpu_dict[im], MPI_COMM_WORLD)
FH_list[im] = dummy[im]
mpi_barrier(MPI_COMM_WORLD)
mpi_barrier(MPI_COMM_WORLD)
if options.local_alignment:
if Blockdata["myid"] == Blockdata["main_node"]:
ali3d_local_params = [None for im in range(len(ptl_list))]
for im in range(len(ptl_list)):
ali3d_local_params[im] = [ptl_list[im]] + plist_dict[
global_dict[ptl_list[im]][0]][global_dict[ptl_list[im]][1]]
write_text_row(
ali3d_local_params,
os.path.join(Tracker["constants"]["masterdir"],
"ali2d_local_params.txt"))
write_text_row(
FH_list,
os.path.join(Tracker["constants"]["masterdir"], "FH_list.txt"))
else:
if Blockdata["myid"] == Blockdata["main_node"]:
write_text_row(
FH_list,
os.path.join(Tracker["constants"]["masterdir"], "FH_list.txt"))
mpi_barrier(MPI_COMM_WORLD)
target_xr = 3
target_yr = 3
if (Blockdata["myid"] == 0):
cmd = "{} {} {} {} {} {} {} {} {} {}".format("sxchains.py", os.path.join(Tracker["constants"]["masterdir"],"class_averages.hdf"),\
os.path.join(Tracker["constants"]["masterdir"],"junk.hdf"),os.path.join(Tracker["constants"]["masterdir"],"ordered_class_averages.hdf"),\
"--circular","--radius=%d"%Tracker["constants"]["radius"] , "--xr=%d"%(target_xr+1),"--yr=%d"%(target_yr+1),"--align", ">/dev/null")
junk = cmdexecute(cmd)
cmd = "{} {}".format(
"rm -rf",
os.path.join(Tracker["constants"]["masterdir"], "junk.hdf"))
junk = cmdexecute(cmd)
from mpi import mpi_finalize
mpi_finalize()
exit()
def do_volume_mrk02(ref_data):
"""
data - projections (scattered between cpus) or the volume. If volume, just do the volume processing
options - the same for all cpus
return - volume the same for all cpus
"""
from EMAN2 import Util
from mpi import mpi_comm_rank, mpi_comm_size, MPI_COMM_WORLD
from filter import filt_table
from reconstruction import recons3d_4nn_MPI, recons3d_4nn_ctf_MPI
from utilities import bcast_EMData_to_all, bcast_number_to_all, model_blank
from fundamentals import rops_table, fftip, fft
import types
# Retrieve the function specific input arguments from ref_data
data = ref_data[0]
Tracker = ref_data[1]
iter = ref_data[2]
mpi_comm = ref_data[3]
# # For DEBUG
# print "Type of data %s" % (type(data))
# print "Type of Tracker %s" % (type(Tracker))
# print "Type of iter %s" % (type(iter))
# print "Type of mpi_comm %s" % (type(mpi_comm))
if (mpi_comm == None): mpi_comm = MPI_COMM_WORLD
myid = mpi_comm_rank(mpi_comm)
nproc = mpi_comm_size(mpi_comm)
try:
local_filter = Tracker["local_filter"]
except:
local_filter = False
#=========================================================================
# volume reconstruction
if (type(data) == types.ListType):
if Tracker["constants"]["CTF"]:
vol = recons3d_4nn_ctf_MPI(myid, data, Tracker["constants"]["snr"], \
symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm, smearstep = Tracker["smearstep"])
else:
vol = recons3d_4nn_MPI (myid, data,\
symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm)
else:
vol = data
if myid == 0:
from morphology import threshold
from filter import filt_tanl, filt_btwl
from utilities import model_circle, get_im
import types
nx = vol.get_xsize()
if (Tracker["constants"]["mask3D"] == None):
mask3D = model_circle(
int(Tracker["constants"]["radius"] * float(nx) /
float(Tracker["constants"]["nnxo"]) + 0.5), nx, nx, nx)
elif (Tracker["constants"]["mask3D"] == "auto"):
from utilities import adaptive_mask
mask3D = adaptive_mask(vol)
else:
if (type(Tracker["constants"]["mask3D"]) == types.StringType):
mask3D = get_im(Tracker["constants"]["mask3D"])
else:
mask3D = (Tracker["constants"]["mask3D"]).copy()
nxm = mask3D.get_xsize()
if (nx != nxm):
from fundamentals import rot_shift3D
mask3D = Util.window(
rot_shift3D(mask3D, scale=float(nx) / float(nxm)), nx, nx,
nx)
nxm = mask3D.get_xsize()
assert (nx == nxm)
stat = Util.infomask(vol, mask3D, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0 / stat[1])
vol = threshold(vol)
Util.mul_img(vol, mask3D)
if (Tracker["PWadjustment"]):
from utilities import read_text_file, write_text_file
rt = read_text_file(Tracker["PWadjustment"])
fftip(vol)
ro = rops_table(vol)
# Here unless I am mistaken it is enough to take the beginning of the reference pw.
for i in xrange(1, len(ro)):
ro[i] = (rt[i] / ro[i])**Tracker["upscale"]
#write_text_file(rops_table(filt_table( vol, ro),1),"foo.txt")
if Tracker["constants"]["sausage"]:
ny = vol.get_ysize()
y = float(ny)
from math import exp
for i in xrange(len(ro)): ro[i] *= \
(1.0+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.10)/0.025)**2)+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.215)/0.025)**2))
if local_filter:
# skip low-pass filtration
vol = fft(filt_table(vol, ro))
else:
if (type(Tracker["lowpass"]) == types.ListType):
vol = fft(
filt_table(filt_table(vol, Tracker["lowpass"]), ro))
else:
vol = fft(
filt_table(
filt_tanl(vol, Tracker["lowpass"],
Tracker["falloff"]), ro))
del ro
else:
if Tracker["constants"]["sausage"]:
ny = vol.get_ysize()
y = float(ny)
ro = [0.0] * (ny // 2 + 2)
from math import exp
for i in xrange(len(ro)): ro[i] = \
(1.0+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.10)/0.025)**2)+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.215)/0.025)**2))
fftip(vol)
filt_table(vol, ro)
del ro
if not local_filter:
if (type(Tracker["lowpass"]) == types.ListType):
vol = filt_table(vol, Tracker["lowpass"])
else:
vol = filt_tanl(vol, Tracker["lowpass"],
Tracker["falloff"])
if Tracker["constants"]["sausage"]: vol = fft(vol)
if local_filter:
from morphology import binarize
if (myid == 0): nx = mask3D.get_xsize()
else: nx = 0
nx = bcast_number_to_all(nx, source_node=0)
# only main processor needs the two input volumes
if (myid == 0):
mask = binarize(mask3D, 0.5)
locres = get_im(Tracker["local_filter"])
lx = locres.get_xsize()
if (lx != nx):
if (lx < nx):
from fundamentals import fdecimate, rot_shift3D
mask = Util.window(
rot_shift3D(mask, scale=float(lx) / float(nx)), lx, lx,
lx)
vol = fdecimate(vol, lx, lx, lx)
else:
ERROR("local filter cannot be larger than input volume",
"user function", 1)
stat = Util.infomask(vol, mask, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0 / stat[1])
else:
lx = 0
locres = model_blank(1, 1, 1)
vol = model_blank(1, 1, 1)
lx = bcast_number_to_all(lx, source_node=0)
if (myid != 0): mask = model_blank(lx, lx, lx)
bcast_EMData_to_all(mask, myid, 0, comm=mpi_comm)
from filter import filterlocal
vol = filterlocal(locres, vol, mask, Tracker["falloff"], myid, 0,
nproc)
if myid == 0:
if (lx < nx):
from fundamentals import fpol
vol = fpol(vol, nx, nx, nx)
vol = threshold(vol)
vol = filt_btwl(vol, 0.38, 0.5) # This will have to be corrected.
Util.mul_img(vol, mask3D)
del mask3D
# vol.write_image('toto%03d.hdf'%iter)
else:
vol = model_blank(nx, nx, nx)
else:
if myid == 0:
#from utilities import write_text_file
#write_text_file(rops_table(vol,1),"goo.txt")
stat = Util.infomask(vol, mask3D, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0 / stat[1])
vol = threshold(vol)
vol = filt_btwl(vol, 0.38, 0.5) # This will have to be corrected.
Util.mul_img(vol, mask3D)
del mask3D
# vol.write_image('toto%03d.hdf'%iter)
# broadcast volume
bcast_EMData_to_all(vol, myid, 0, comm=mpi_comm)
#=========================================================================
return vol