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 lattice_test(integrate_lp, xds_inp_file): images, phi, cell, records = rj_parse_integrate_lp( open(integrate_lp).readlines()) # next work through the XDS.INP file to get the proper name template # out... nt = None distance = None for record in open(xds_inp_file, 'r').readlines(): if 'NAME_TEMPLATE_OF_DATA_FRAMES' in record: nt = record.strip() if 'DETECTOR_DISTANCE' in record: distance = record.strip() if not nt: raise RuntimeError, 'filename template not found in %s' % xds_inp_file if not distance: raise RuntimeError, 'distance not found in %s' % xds_inp_file r_new = [distance] for r in records: if not 'NAME_TEMPLATE_OF_DATA_FRAMES' in r: r_new.append(r) else: r_new.append(nt) records = r_new # ok, in here need to rerun XDS with all of the data from all of # the images and the triclinic target cell, then parse out the # solutions from the CORRECT.LP file (applying the cell constants - # done in the parser) and then use *these* as the target, as the # lattice symmetry code (interestingly) does not always give the # right answer... standard = [ 'JOB=CORRECT', 'MAXIMUM_NUMBER_OF_PROCESSORS=4', 'CORRECTIONS=!', 'REFINE(CORRECT)=CELL', 'OVERLOAD=65000', 'DIRECTION_OF_DETECTOR_X-AXIS=1.0 0.0 0.0', 'DIRECTION_OF_DETECTOR_Y-AXIS=0.0 1.0 0.0', 'TRUSTED_REGION=0.0 1.41' ] # first get the list of possible lattices - do this by running CORRECT # with all of the images, then looking at the favourite settings for the # P1 result (or something) - meh. fout = open('XDS.INP', 'w') for record in standard: fout.write('%s\n' % record) for record in records: fout.write('%s\n' % record) fout.write('DATA_RANGE= %d %d\n' % images) fout.write('OSCILLATION_RANGE= %.2f\n' % phi) fout.write( 'UNIT_CELL_CONSTANTS= %.2f %.2f %.2f %.2f %.2f %.2f\n' % tuple(cell)) fout.write('SPACE_GROUP_NUMBER=%d\n' % 1) fout.close() output = rj_run_job('xds_par', [], []) # read CORRECT.LP to get the right solutions... result = rj_parse_xds_correct_lp(open('CORRECT.LP', 'r').readlines()) for lattice in result: cp = '%.2f %.2f %.2f %.2f %.2f %.2f' % result[lattice]['cell'] # print '%s %s' % (lattice, cp) # result = lattice_symmetry(cell) lattices = sort_lattices(result) # then iterate through them... data = { } for l in lattices: data[l] = { } c = result[l]['cell'] fout = open('XDS.INP', 'w') for record in standard: fout.write('%s\n' % record) for record in records: fout.write('%s\n' % record) fout.write('DATA_RANGE= %d %d\n' % (images)) fout.write('OSCILLATION_RANGE= %.2f\n' % phi) fout.write( 'UNIT_CELL_CONSTANTS= %.2f %.2f %.2f %.2f %.2f %.2f\n' % tuple(c)) fout.write('SPACE_GROUP_NUMBER=%d\n' % lattice_spacegroup(l)) fout.close() output = rj_run_job('xds_par', [], []) # now read out the records I want from CORRECT.LP... rmsd = None rmsp = None for record in open('CORRECT.LP').readlines(): if 'STANDARD DEVIATION OF SPOT POSITION' in record: rmsd = float(record.split()[-1]) if 'STANDARD DEVIATION OF SPINDLE POSITION' in record: rmsp = float(record.split()[-1]) if not rmsp or not rmsd: raise RuntimeError, 'refinement failed' print '%s rmsd %f rmsp %f' % (l, rmsd, rmsp)
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'])
def lattice_test(integrate_lp, xds_inp_file): images, phi, cell, records = rj_parse_integrate_lp( open(integrate_lp).readlines()) # next work through the XDS.INP file to get the proper name template # out... nt = None distance = None for record in open(xds_inp_file, 'r').readlines(): if 'NAME_TEMPLATE_OF_DATA_FRAMES' in record: nt = record.strip() if 'DETECTOR_DISTANCE' in record: distance = record.strip() if not nt: raise RuntimeError, 'filename template not found in %s' % xds_inp_file if not distance: raise RuntimeError, 'distance not found in %s' % xds_inp_file r_new = [distance] for r in records: if not 'NAME_TEMPLATE_OF_DATA_FRAMES' in r: r_new.append(r) else: r_new.append(nt) records = r_new # ok, in here need to rerun XDS with all of the data from all of # the images and the triclinic target cell, then parse out the # solutions from the CORRECT.LP file (applying the cell constants - # done in the parser) and then use *these* as the target, as the # lattice symmetry code (interestingly) does not always give the # right answer... standard = [ 'JOB=CORRECT', 'MAXIMUM_NUMBER_OF_PROCESSORS=4', 'CORRECTIONS=!', 'REFINE(CORRECT)=CELL', 'OVERLOAD=65000', 'DIRECTION_OF_DETECTOR_X-AXIS=1.0 0.0 0.0', 'DIRECTION_OF_DETECTOR_Y-AXIS=0.0 1.0 0.0', 'TRUSTED_REGION=0.0 1.41' ] # first get the list of possible lattices - do this by running CORRECT # with all of the images, then looking at the favourite settings for the # P1 result (or something) - meh. fout = open('XDS.INP', 'w') for record in standard: fout.write('%s\n' % record) for record in records: fout.write('%s\n' % record) fout.write('DATA_RANGE= %d %d\n' % images) fout.write('OSCILLATION_RANGE= %.2f\n' % phi) fout.write( 'UNIT_CELL_CONSTANTS= %.2f %.2f %.2f %.2f %.2f %.2f\n' % tuple(cell)) fout.write('SPACE_GROUP_NUMBER=%d\n' % 1) fout.close() output = rj_run_job('xds_par', [], []) # read CORRECT.LP to get the right solutions... result = rj_parse_xds_correct_lp(open('CORRECT.LP', 'r').readlines()) for lattice in result: cp = '%.2f %.2f %.2f %.2f %.2f %.2f' % result[lattice]['cell'] # print '%s %s' % (lattice, cp) # result = lattice_symmetry(cell) lattices = sort_lattices(result) # then iterate through them... data = { } for l in lattices: data[l] = { } c = result[l]['cell'] # print 'Lattice: %s' % l # print 'Cell: %.2f %.2f %.2f %.2f %.2f %.2f' % tuple(c) # then iterate through the image ranges w = nint(10.0/phi) m = nint((images[1] - images[0] + 1) / w) for j in range(m): start = j * w + 1 end = j * w + w data[l][j] = { } fout = open('XDS.INP', 'w') for record in standard: fout.write('%s\n' % record) for record in records: fout.write('%s\n' % record) fout.write('DATA_RANGE= %d %d\n' % (start, end)) fout.write('OSCILLATION_RANGE= %.2f\n' % phi) fout.write( 'UNIT_CELL_CONSTANTS= %.2f %.2f %.2f %.2f %.2f %.2f\n' % tuple(c)) fout.write('SPACE_GROUP_NUMBER=%d\n' % lattice_spacegroup(l)) fout.close() output = rj_run_job('xds_par', [], []) # now read out the records I want from CORRECT.LP... rmsd = None rmsp = None for record in open('CORRECT.LP').readlines(): if 'STANDARD DEVIATION OF SPOT POSITION' in record: rmsd = float(record.split()[-1]) if 'STANDARD DEVIATION OF SPINDLE POSITION' in record: rmsp = float(record.split()[-1]) if not rmsp or not rmsd: raise RuntimeError, 'refinement failed' data[l][j] = {'d':rmsd, 'p':rmsp} # now tabulate the results for j in range(m): record = '%d' % j for l in lattices[1:]: record += ' %.3f %.3f' % (data[l][j]['d'] / data['aP'][j]['d'], data[l][j]['p'] / data['aP'][j]['p']) print record # now print out the averages, sd's recordm = 'M' records = 'S' sigma = { } for l in lattices[1:]: values = [(data[l][j]['d'] / data['aP'][j]['d']) for j in range(m)] md, sd = meansd(values) values = [(data[l][j]['p'] / data['aP'][j]['p']) for j in range(m)] mp, sp = meansd(values) recordm += ' %.3f %.3f' % (md, mp) records += ' %.3f %.3f' % (sd, sp) sigma[l] = { } if sd > 0: sigma[l]['d'] = ((md - 1) / sd) else: sigma[l]['d'] = 0.0 if sp > 0: sigma[l]['p'] = ((mp - 1) / sp) else: sigma[l]['p'] = 0.0 print recordm print records for l in lattices[1:]: d = sigma[l]['d'] p = sigma[l]['p'] print '= %s %.3f %.3f' % (l, d, p)