def no_images(labelit_log):
# 3 images per wedge, maximum of 30 => 1 to 10 wedges.
beam, lattice, metric, cell, image = rj_parse_labelit_log_file(labelit_log)
template, directory = rj_get_template_directory(image)
images = rj_find_matching_images(image)
phi = rj_get_phi(image)
if lattice == 'aP':
raise RuntimeError, 'triclinic lattices useless'
# right, what I want to do is autoindex with images at 0, 45, 90 or
# thereabouts (in P1), then do the cell refinement, then score the
# resulting cell constants
ai_images = calculate_images_ai(images, phi, 3)
metrics = []
for count in range(1, 10):
result = calculate_images(images, phi, count + 1)
# first autoindex commands
commands = [
'template %s' % template,
'directory %s' % directory,
'beam %f %f' % beam]
commands.append('symm P1')
for image in ai_images:
commands.append('autoindex dps refine image %d' % image)
commands.append('mosaic estimate')
commands.append('go')
# the cell refinement commands
commands.append('postref multi segments 3')
for pair in result:
commands.append('process %d %d' % pair)
commands.append('go')
output = rj_run_job('ipmosflm-7.0.3', [], commands)
cell, mosaic = rj_parse_mosflm_cr_log(output)
result = lattice_symmetry(cell)
l = sort_lattices(result.keys())[-1]
if l != lattice:
raise RuntimeError, 'cell refinement gave wrong lattice'
metrics.append(result[l]['penalty'])
return metrics
def phi_spacing(labelit_log):
beam, lattice, metric, cell, image = rj_parse_labelit_log_file(labelit_log)
template, directory = rj_get_template_directory(image)
images = rj_find_matching_images(image)
phi = rj_get_phi(image)
if lattice == 'aP':
raise RuntimeError, 'triclinic lattices useless'
# then copy the dataset_preferences.py somewhere safe
if os.path.exists('dataset_preferences.py'):
shutil.copyfile('dataset_preferences.py',
'dataset_preferences.bak')
# write out a dataset preferences file
fout = open('dataset_preferences.py', 'w')
fout.write('beam = (%f, %f)\n' % beam)
fout.write('wedgelimit = 3\n')
fout.write('beam_search_scope = 1.0\n')
fout.close()
# generate the list of phi values...
phis = [float(j + 1) for j in range(5, 45)]
image_numbers = []
for p in phis:
result = calculate_images(images, phi, p)
if not result in image_numbers:
image_numbers.append(result)
# now run labelit with phi spacing 6-45
metrics = []
spacings = []
for i_n in image_numbers:
spacing = phi * (i_n[2] - i_n[1])
image_names = [rj_image_name(template, directory, i) for i in i_n]
output = rj_run_job('labelit.screen --index_only',
image_names, [])
b, l, m, c, i = rj_parse_labelit_log(output)
if l != lattice:
raise RuntimeError, 'incorrect result with %d images' % (count + 1)
metrics.append(m)
spacings.append(spacing)
if os.path.exists('dataset_preferences.bak'):
shutil.copyfile('dataset_preferences.bak', 'dataset_preferences.py')
return metrics, spacings
def no_images(labelit_log):
# first parse this
beam, lattice, metric, cell, image = rj_parse_labelit_log_file(labelit_log)
template, directory = rj_get_template_directory(image)
images = rj_find_matching_images(image)
phi = rj_get_phi(image)
if lattice == 'aP':
raise RuntimeError, 'triclinic lattices useless'
# then copy the dataset_preferences.py somewhere safe
if os.path.exists('dataset_preferences.py'):
shutil.copyfile('dataset_preferences.py',
'dataset_preferences.bak')
# write out a dataset preferences file
fout = open('dataset_preferences.py', 'w')
fout.write('beam = (%f, %f)\n' % beam)
fout.write('wedgelimit = 15\n')
fout.write('beam_search_scope = 1.0\n')
fout.close()
# now run labelit with 1 - 15 images
metrics = []
times = []
for count in range(15):
result = calculate_images(images, phi, count + 1)
image_names = [rj_image_name(template, directory, i) for i in result]
t0 = time.time()
output = rj_run_job('labelit.screen --index_only',
image_names, [])
t1 = time.time()
times.append((t1 - t0))
b, l, m, c, i = rj_parse_labelit_log(output)
if l != lattice:
raise RuntimeError, 'incorrect result with %d images' % (count + 1)
metrics.append(m)
if os.path.exists('dataset_preferences.bak'):
shutil.copyfile('dataset_preferences.bak', 'dataset_preferences.py')
return metrics, times
def cr_test(labelit_log):
beam, lattice, metric, cell, image = rj_parse_labelit_log_file(labelit_log)
lattices, cells = rj_parse_labelit_log_lattices(
open(labelit_log).readlines())
template, directory = rj_get_template_directory(image)
images = rj_find_matching_images(image)
phi = rj_get_phi(image)
if lattice == 'aP':
raise RuntimeError, 'triclinic lattices useless'
wedges = calculate_images(images, phi)
ai_images = calculate_images_ai(images, phi, 3)
# run a quick autoindex (or re-read the labelit log file above) to
# generate the list of possible unit cell etc.
rmsds_all = { }
# then loop over these
for lattice in lattices:
commands = [
'template %s' % template,
'directory %s' % directory,
'beam %f %f' % beam]
commands.append('symm %d' % lattice_spacegroup(lattice))
commands.append('cell %f %f %f %f %f %f' % tuple(cells[lattice]))
for image in ai_images:
commands.append('autoindex dps refine image %d' % image)
commands.append('mosaic estimate')
commands.append('go')
# the cell refinement commands
commands.append('postref multi segments 3')
for pair in wedges:
commands.append('process %d %d' % pair)
commands.append('go')
for c in commands:
# print c
pass
output = rj_run_job('ipmosflm-7.0.3', [], commands)
images, rmsds = rj_parse_mosflm_cr_log_rmsd(output)
rmsds_all[lattice] = rmsds
# and finally calculate the RMSD ratios.
# break up by lattice, image and cycle
for lattice in lattices[:-1]:
print lattice
values = []
for cycle in rmsds_all[lattice]:
if not cycle in rmsds_all['aP']:
continue
record = '%3d' % cycle
for j in range(len(images)):
record += ' %.3f' % (rmsds_all[lattice][cycle][j] /
rmsds_all['aP'][cycle][j])
values.append((rmsds_all[lattice][cycle][j] /
rmsds_all['aP'][cycle][j]))
print record
m, s = meansd(values)
print ':: %s %.3f %.3f' % (lattice, m, s)
def phi_spacing(labelit_log):
# 3 images per wedge, maximum of 30 => 1 to 10 wedges.
beam, lattice, metric, cell, image = rj_parse_labelit_log_file(labelit_log)
template, directory = rj_get_template_directory(image)
images = rj_find_matching_images(image)
phi = rj_get_phi(image)
if lattice == 'aP':
raise RuntimeError, 'triclinic lattices useless'
# right, what I want to do is autoindex with images at 0, 45, 90 or
# thereabouts (in P1), then do the cell refinement, then score the
# resulting cell constants
ai_images = calculate_images_ai(images, phi, 3)
metrics = []
spacings = []
phis = [float(j + 1) for j in range(10, 45)]
image_numbers = []
for p in phis:
result = calculate_images(images, phi, p)
if phi * (result[-1][-1] - result[0][0] + 1) > 90.0:
continue
if not result in image_numbers:
image_numbers.append(result)
for result in image_numbers:
# first autoindex commands
spacing = nint(phi * (result[1][0] - result[0][0]))
spacings.append(spacing)
commands = [
'template %s' % template,
'directory %s' % directory,
'beam %f %f' % beam]
commands.append('symm P1')
for image in ai_images:
commands.append('autoindex dps refine image %d' % image)
commands.append('mosaic estimate')
commands.append('go')
# the cell refinement commands
commands.append('postref multi segments 3')
for pair in result:
commands.append('process %d %d' % pair)
commands.append('go')
output = rj_run_job('ipmosflm-7.0.3', [], commands)
try:
cell, mosaic = rj_parse_mosflm_cr_log(output)
except RuntimeError, e:
for record in output:
print record[:-1]
raise e
result = lattice_symmetry(cell)
l = sort_lattices(result.keys())[-1]
if l != lattice:
raise RuntimeError, 'cell refinement gave wrong lattice'
metrics.append(result[l]['penalty'])
