def julien( ref_data ):
from utilities import print_msg
from filter import fit_tanh, filt_tanl
from utilities import center_2D
# Prepare the reference in 2D 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 (FRC) to reference image:
global ref_ali2d_counter
ref_ali2d_counter += 1
ref_ali2d_counter = ref_ali2d_counter % 50
print_msg("ref_ali2d #%6d\n"%(ref_ali2d_counter))
fl = min(0.1+ref_ali2d_counter*0.003, 0.4)
aa = 0.1
msg = "Tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n"%(fl, aa)
print_msg(msg)
tavg = filt_tanl(ref_data[2], fl, aa)
cs = [0.0]*2
if ref_data[1] > 0:
tavg, cs[0], cs[1] = center_2D(tavg, ref_data[1])
msg = "Center x = %10.3f, y = %10.3f\n"%(cs[0], cs[1])
print_msg(msg)
return tavg, cs
def ref_ali2d_c(ref_data):
from utilities import print_msg
from filter import fit_tanh, filt_tanl
from utilities import center_2D
# Prepare the reference in 2D 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 (FRC) to reference image:
global ref_ali2d_counter
ref_ali2d_counter += 1
print_msg("ref_ali2d #%6d\n" % (ref_ali2d_counter))
fl = min(0.1 + ref_ali2d_counter * 0.003, 0.4)
aa = 0.1
msg = "Tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n" % (
fl, aa)
print_msg(msg)
tavg = filt_tanl(ref_data[2], fl, aa)
cs = [0.0] * 2
if (ref_data[1] > 0):
tavg, cs[0], cs[1] = center_2D(tavg, ref_data[1])
msg = "Center x = %10.3f, y = %10.3f\n" % (cs[0], cs[1])
print_msg(msg)
return tavg, cs
def ref_random(ref_data):
from utilities import print_msg
from filter import fit_tanh, filt_tanl
from utilities import center_2D
# Prepare the reference in 2D 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 (FRC) to reference image:
global ref_ali2d_counter
ref_ali2d_counter += 1
print_msg("ref_ali2d #%6d\n" % (ref_ali2d_counter))
"""
fl, aa = fit_tanh(ref_data[3])
msg = "Tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n"%(fl, aa)
print_msg(msg)
tavg = filt_tanl(ref_data[2], fl, aa)
"""
# ONE CAN USE BUTTERWORTH FILTER
#lowfq, highfq = filt_params( ref_data[3], low = 0.1)
#tavg = filt_btwl( ref_data[2], lowfq, highfq)
#msg = "Low frequency = %10.3f High frequency = %10.3f\n"%(lowfq, highfq)
#print_msg(msg)
# ONE CAN CHANGE THE MASK AS THE PROGRAM PROGRESSES
#from morphology import adaptive_mask
#ref_data[0] = adaptive_mask(tavg)
# CENTER
cs = [0.0] * 2
tavg, cs[0], cs[1] = center_2D(ref_data[2], ref_data[1])
'''
from math import exp
nx = tavg.get_xsize()
ft = []
good = True
for i in xrange(nx):
if(good):
ex = exp((float(i)/float(nx))**2/2.0/0.12**2)
if(ex>100.): good = False
ft.append(ex)
from filter import filt_table
tavg = filt_table(tavg, ft)
'''
if (ref_data[1] > 0):
msg = "Center x = %10.3f Center y = %10.3f\n" % (
cs[0], cs[1])
print_msg(msg)
return tavg, cs
def ref_random( ref_data ):
from utilities import print_msg
from filter import fit_tanh, filt_tanl
from utilities import center_2D
# Prepare the reference in 2D 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 (FRC) to reference image:
global ref_ali2d_counter
ref_ali2d_counter += 1
print_msg("ref_ali2d #%6d\n"%(ref_ali2d_counter))
"""
fl, aa = fit_tanh(ref_data[3])
msg = "Tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n"%(fl, aa)
print_msg(msg)
tavg = filt_tanl(ref_data[2], fl, aa)
"""
# ONE CAN USE BUTTERWORTH FILTER
#lowfq, highfq = filt_params( ref_data[3], low = 0.1)
#tavg = filt_btwl( ref_data[2], lowfq, highfq)
#msg = "Low frequency = %10.3f High frequency = %10.3f\n"%(lowfq, highfq)
#print_msg(msg)
# ONE CAN CHANGE THE MASK AS THE PROGRAM PROGRESSES
#from morphology import adaptive_mask
#ref_data[0] = adaptive_mask(tavg)
# CENTER
cs = [0.0]*2
tavg, cs[0], cs[1] = center_2D(ref_data[2], ref_data[1])
'''
from math import exp
nx = tavg.get_xsize()
ft = []
good = True
for i in xrange(nx):
if(good):
ex = exp((float(i)/float(nx))**2/2.0/0.12**2)
if(ex>100.): good = False
ft.append(ex)
from filter import filt_table
tavg = filt_table(tavg, ft)
'''
if(ref_data[1] > 0):
msg = "Center x = %10.3f Center y = %10.3f\n"%(cs[0], cs[1])
print_msg(msg)
return tavg, cs