def driver(image_file,
algorithm,
binary_threshold,
canny_low,
canny_high,
window_height,
window_width,
tracking,
gap,
repeat,
transpose,
debug,
quiet):
align = Alignment(image_file)
params = {
'binary_threshold': binary_threshold,
'canny_thresh_low': canny_low,
'canny_thresh_high': canny_high,
'window_height': window_height,
'window_width' : window_width,
'gap': gap,
'transpose': transpose,
'debug': debug,
'quiet': quiet
}
if tracking:
tracking(params, algorithm, repeat)
elif algorithm:
print(align.compute_center(algorithm, params));
def parseContents(contents, section):
if section==1:
readNames = []
contained = {}
for line in contents:
if line[0]!=' ':
readNames.append(line.strip())
else:
tokens = line.strip().split()
readNames.append(tokens[0])
contained[tokens[0]] = tokens[-1]
return readNames, contained
elif section==2:
aln = Alignment()
iSeqStart = -1
L = 0
for line in contents:
if line==Tokens.ruler:
# Ruler line
iSeqStart = line.find('.')-4 # = 22 ???
if iSeqStart!=22: print "!!! iSeqStart:", iSeqStart
elif line[0]==' ':
# Separator line
pass
else:
line = line.rstrip()
name = line[0:iSeqStart].strip()
seq = line[iSeqStart:]
if name!='consensus':
if not name in aln.seqDict:
seq = seq.replace(' ', '.')
seq = '.'*L + seq
aln.append(name, seq)
else:
aln.append(name, seq)
L = len(aln['consensus'])
# Pad short sequences with '.'
for name in aln:
aln[name] = aln[name] + '.'*(L-len(aln[name]))
return aln
else:
raise Exception("cap3.parseContents")
def constrain_mutant_to_wt(mutant_pose,
wt_pose,
mut_focus_residues,
wt_focus_residues,
constrain=True,
shell=6):
aligner = Alignment(mutant_pose, wt_pose)
# Get chain info (this is a roundabout way of getting this and
# should probably be refactored at some point.)
# Initially this just used the Rosetta chain, but (older?) versions
# of PyRosetta do not seem to support that.
tchain = mutant_pose.pdb_info().chain(mut_focus_residues[0])
mchain = wt_pose.pdb_info().chain(wt_focus_residues[0])
# Create a selection shell
aligner.create_shell(shell,
mut_focus_residues,
mobile_focus_list=wt_focus_residues,
tchain=tchain,
mchain=mchain)
aligner.match_align()
if constrain:
alignment_dict = {}
assert (len(mut_focus_residues) == len(wt_focus_residues))
for i in range(0, len(mut_focus_residues)):
alignment_dict[(wt_focus_residues[i],
mut_focus_residues[i])] = ['N', 'C', 'CA']
csts = constraints_from_pose(aligner.mobile, aligner.target,
alignment_dict)
for cst in csts:
aligner.target.add_constraint(cst)
return aligner
#!/usr/bin/env python
"""
stockholm2phylip.py <stockholm filename> <phylip filename>
Author: Tony Papenfuss
Date: Sun May 6 13:34 EST 2007
"""
import os, sys
from align import Alignment
iFilename = sys.argv[1]
oFilename = sys.argv[2]
alignment = Alignment.loadStockholm(iFilename)
for name in alignment:
alignment[name] = alignment[name].replace('.', '-')
alignment.savePhylip(oFilename)
def main():
# m = examples["diff"]
# print PermanentSampler(0.5,0.1).estimate_permanent(m)
# r_perm = ryser(m)
# #n_perm = naive_permanent(m)
# print r_perm
# #print n_perm
from align import Align
from align import Alignment
import sys
mode = model_names[sys.argv[3]]
align = Align.from_files("data/eng-fr.full.fr", "data/eng-fr.full.en")
e_types, f_types = align.types()
prob_model = dist.ProbModel(f_types, e_types)
test_align = Align.from_files("data/eng-fr.dev.fr", "data/eng-fr.dev.en",
True)
gold_align = Alignment.read_alignments("data/eng-fr.dev.align")
if sys.argv[1] == "train":
dists = prob_model.rand_dists_sparse()
instances = align.instances()
test_instances = test_align.instances()
for r in range(20):
if mode == MANYTOONE:
score, _ = dev_assess(test_instances, gold_align, dists,
prob_model, viterbi_align_manyone)
else:
score, _ = dev_assess(test_instances, gold_align, dists,
prob_model, viterbi_align_oneone)
dists = em(instances, dists, mode, prob_model)
#print "Dist 'le'"
#for i, lscore in enumerate(dists['le']):
# if lscore > 1e-4:
# print i, align.eng_to_ind(i), lscore
print "Score is:", score
final_dist = dists
pickle.dump(final_dist, open(sys.argv[2], 'wb'))
elif sys.argv[1] == "test":
dists = pickle.load(open(sys.argv[2], 'rb'))
instances = test_align.instances()
if mode == MANYTOONE:
score, alignments = dev_assess(instances, gold_align, dists,
prob_model, viterbi_align_manyone)
else:
score, alignments = dev_assess(instances, gold_align, dists,
prob_model, viterbi_align_oneone)
print score
f_out = open("out.f", 'w')
e_out = open("out.e", 'w')
a_out = open("out.a", 'w')
gold_out = open("out.gold.a", 'w')
for ins, align in zip(instances, alignments):
print >> f_out, " ".join(ins.f)
print >> e_out, " ".join(ins.e)
print >> a_out, " ".join([str(e) + "-" + str(f) for e, f in align])
print >> gold_out, " ".join(
[str(e) + "-" + str(f) for e, f in gold_align[ins.num]])
import cv2
from align import Alignment
X = Alignment()
def read(path):
img = cv2.imread(path)
height = img.shape[0]
width = img.shape[1]
ratio = height / width
return cv2.resize(img, (int(500 * ratio), 500))
def draw_rec(event, x, y, flags, param):
global ix, iy, ex, ey, drawing, mode
if event == cv2.EVENT_LBUTTONDOWN:
ix, iy = x, y
elif event == cv2.EVENT_LBUTTONUP:
ex, ey = x, y
def get_crop_size(image):
cv2.namedWindow('draw_rectangle')
cv2.setMouseCallback('draw_rectangle', draw_rec, image)
print("Choose your area of interest!")
while 1:
cv2.imshow('draw_rectangle', image)
k = cv2.waitKey(1) & 0xFF
if k == ord('a'):
def tracking(params, algorithm='pin', repeat=1):
#### ENABLE TAB COMPLETION
readline.parse_and_bind('tab: complete')
#################################################
pixel_size = 1.172
## um / pixel
slit_center_x = 1000
slit_center_y = 600
#################################################
### MOTOR SETTINGS
#################################################
mtr_samXE = PyEpics.Motor('1ide1:m34')
mtr_samYE = PyEpics.Motor('1ide1:m35')
mtr_samZE = PyEpics.Motor('1ide1:m36')
mtr_samTh = PyEpics.Motor('1ide1:m86')
mtr_samChi = PyEpics.Motor('1ide1:m87')
mtr_samOme = PyEpics.Motor('1ide:m9')
mtr_aeroXE = PyEpics.Motor('1ide1:m101')
mtr_aeroZE = PyEpics.Motor('1ide1:m102')
#################################################
pname = '/home/beams/S1IDUSER/mnt/s1c/mli_nov19/tomo'
for i in range(repeat):
#################################################
### ALIGN ROTATION AXIS TO THE HS
#################################################
### MOVE STAGE TO 0
mtr_samOme.move(0, wait=True)
### TAKE AN IMAGE AT OME 0
PyEpics.caput('1idPG1:cam1:Acquire', 1)
time.sleep(3)
fname = PyEpics.caget('1idPG1:TIFF1:FileName_RBV', 'str') + "_%06d" % (
PyEpics.caget('1idPG1:TIFF1:FileNumber_RBV') - 1) + '.tif'
pfname = os.path.join(pname, fname)
print(pfname)
align0 = Alignment(pfname)
x0, y0 = align0.compute_center(algorithm, params)
print(f"pin x,y position when omega is 0 : (x = {x0}, y = {y0}")
if (x0 == -1) or (y0 == -1):
print("Alignment failed at zero degress")
break
### MOVE STAGE TO 180
mtr_samOme.move(180, wait=True)
### TAKE AN IMAGE AT OME 180
PyEpics.caput('1idPG1:cam1:Acquire', 1)
time.sleep(3)
fname = PyEpics.caget('1idPG1:TIFF1:FileName_RBV', 'str') + "_%06d" % (
PyEpics.caget('1idPG1:TIFF1:FileNumber_RBV') - 1) + '.tif'
pfname = os.path.join(pname, fname)
align180 = Alignment(pfname)
x180, y180 = align180.compute_center(algorithm, params)
if (x180 == -1) or (y180 == -1):
print("Alignment failed at 180 degrees")
break
print(f"pin x,y position when omega is 180 : (X = {x180}, y = {180})")
### COMPUTE MOTIONS TO MAKE
mid_x = (x180 + x0) / 2
half_delta_x = (x180 - x0) / 2
print(mid_x)
print(half_delta_x)
### NEED TO CHECK / FIGURE OUT THE SIGNS ON THESE
aeroXE_motion = ((mid_x - slit_center_x) * pixel_size) / 1000
samXE_motion = -(half_delta_x * pixel_size) / 1000
print('motions to execute')
print(aeroXE_motion)
print(samXE_motion)
mtr_aeroXE.move(aeroXE_motion, relative=True, wait=True)
mtr_samXE.move(samXE_motion, relative=True, wait=True)
def main():
# m = examples["diff"]
# print PermanentSampler(0.5,0.1).estimate_permanent(m)
# r_perm = ryser(m)
# #n_perm = naive_permanent(m)
# print r_perm
# #print n_perm
from align import Align
from align import Alignment
import sys
mode = model_names[sys.argv[3]]
align = Align.from_files("data/eng-fr.full.fr","data/eng-fr.full.en")
e_types, f_types = align.types()
prob_model = dist.ProbModel(f_types, e_types)
test_align = Align.from_files("data/eng-fr.dev.fr","data/eng-fr.dev.en", True)
gold_align= Alignment.read_alignments("data/eng-fr.dev.align")
if sys.argv[1] == "train":
dists = prob_model.rand_dists_sparse()
instances = align.instances()
test_instances = test_align.instances()
for r in range(20):
if mode == MANYTOONE:
score,_ = dev_assess(test_instances, gold_align, dists, prob_model, viterbi_align_manyone)
else:
score,_ = dev_assess(test_instances, gold_align, dists, prob_model, viterbi_align_oneone)
dists = em(instances, dists, mode, prob_model)
#print "Dist 'le'"
#for i, lscore in enumerate(dists['le']):
# if lscore > 1e-4:
# print i, align.eng_to_ind(i), lscore
print "Score is:", score
final_dist = dists
pickle.dump(final_dist, open(sys.argv[2], 'wb'))
elif sys.argv[1] == "test":
dists = pickle.load(open(sys.argv[2], 'rb'))
instances = test_align.instances()
if mode == MANYTOONE:
score,alignments = dev_assess(instances, gold_align, dists, prob_model, viterbi_align_manyone)
else:
score,alignments = dev_assess(instances, gold_align, dists, prob_model, viterbi_align_oneone)
print score
f_out = open("out.f", 'w')
e_out = open("out.e", 'w')
a_out = open("out.a", 'w')
gold_out = open("out.gold.a", 'w')
for ins,align in zip(instances,alignments):
print >>f_out, " ".join( ins.f)
print >>e_out, " ".join(ins.e)
print >>a_out, " ".join([str(e)+"-"+str(f) for e, f in align])
print >>gold_out, " ".join([str(e)+"-"+str(f) for e, f in gold_align[ins.num]])
#!/usr/bin/env python
"""
stockholm2phylip.py <stockholm filename> <phylip filename>
Author: Tony Papenfuss
Date: Sun May 6 13:34 EST 2007
"""
import os, sys
from align import Alignment
iFilename = sys.argv[1]
oFilename = sys.argv[2]
alignment = Alignment.loadStockholm(iFilename)
for name in alignment:
alignment[name] = alignment[name].replace('.', '-')
alignment.savePhylip(oFilename)
