def no_images(xds_inp):
firstlast, phi, records = rj_parse_idxref_xds_inp(
open(xds_inp, 'r').readlines())
images = [j for j in range(firstlast[0], firstlast[1] + 1)]
# first run with all images, then define the 'correct' lattice
# as the one which results from this autoindex step.
xds_inp = open('XDS.INP', 'w')
for record in records:
xds_inp.write('%s\n' % record)
xds_inp.write('SPOT_RANGE= %d %d\n' % firstlast)
xds_inp.close()
output = rj_run_job('xds', [], [])
cell = rj_parse_idxref_lp(open('IDXREF.LP', 'r').readlines())
result = lattice_symmetry(cell)
lattice = sort_lattices(result.keys())[-1]
score = result[lattice]['penalty']
metrics = []
for count in range(10):
result = calculate_images(images, phi, count + 1)
xds_inp = open('XDS.INP', 'w')
for record in records:
xds_inp.write('%s\n' % record)
for pair in result:
xds_inp.write('SPOT_RANGE= %d %d\n' % pair)
xds_inp.close()
output = rj_run_job('xds', [], [])
cell = rj_parse_idxref_lp(open('IDXREF.LP', 'r').readlines())
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, score
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(xds_inp):
firstlast, phi, records = rj_parse_idxref_xds_inp(
open(xds_inp, 'r').readlines())
images = [j for j in range(firstlast[0], firstlast[1] + 1)]
# first run with all images, then define the 'correct' lattice
# as the one which results from this autoindex step.
xds_inp = open('XDS.INP', 'w')
for record in records:
xds_inp.write('%s\n' % record)
xds_inp.write('SPOT_RANGE= %d %d\n' % firstlast)
xds_inp.close()
output = rj_run_job('xds', [], [])
cell = rj_parse_idxref_lp(open('IDXREF.LP', 'r').readlines())
result = lattice_symmetry(cell)
lattice = sort_lattices(result.keys())[-1]
score = result[lattice]['penalty']
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:
spacing = nint(phi * (result[1][0] - result[0][0]))
spacings.append(spacing)
xds_inp = open('XDS.INP', 'w')
for record in records:
xds_inp.write('%s\n' % record)
for pair in result:
xds_inp.write('SPOT_RANGE= %d %d\n' % pair)
xds_inp.close()
output = rj_run_job('xds', [], [])
cell = rj_parse_idxref_lp(open('IDXREF.LP', 'r').readlines())
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, spacings, score
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'])
