POINTLESS & SCALA Phil Evans

POINTLESS What does it do? 1. Determination of Laue group & space group from unmerged data i. Finds highest symmetry lattice consistent with unit cell ii. Scores each potential rotational symmetry operator in lattice, using correlation coefficient on normalised intensities E 2, with a derived “probability” iii. Scores all combinations of symmetry operators to derive a probability for each point group which is a sub-group of the lattice group (hence find “Laue group” = point group + lattice centering) iv. Scores potential systematic absences to detect screw axes, using a Fourier analysis of I/σ(I), hence assign a probability to possible space groups, if possible 1. In cases where there are alternative indexing conventions, match the indexing to a reference file (merged or unmerged) 2. Reindex or change space group (cf program REINDEX) 3. Just sort one or more input files for Scala (cf SORTMTZ)

POINTLESS New features Input from XDS & Scalepack formats XDS_ASCII.HKL or INTEGRATE.HKL Note the Scalepack output is still not suitable for proper scaling in Scala, since geometric information is lost. Multiple input files, explicit or with wild-cards ★ Checks for consistent indexing between files or file series ★ Automatic renumbering of batches to make them unique (a long-standing irritation) Defaults to IUCr standard settings: can be overridden ★ a < b < c for primitive orthorhombic, allows eg P ★ I2 in place of C2 to give smallest β angle Choose solution from previous run Exclude batch ranges

POINTLESS Improved scoring schemes 1. Probability scoring uses Lorentzian distribution (larger tails than Gaussian) 2. Systematic absence scoring uses intensities “corrected” by subtraction of small fraction (0.02) of their neighbour, to allow for very strong reflections bleeding into absent neighbours. Most reflections are unaffected. Fourier I/sig(I) Corrected I’/sig(I) term Peak Probability Peak Probability * Correction gives higher peaks, larger probabilities

POINTLESS CCP4i interface Multiple file input, same dataset Options for setting General options

POINTLESS SCALEPACK example Scalepack files do not include the unit cell, so this must be given Result displayed by Baubles

POINTLESS Consistent indexing to reference file (merged or unmerged) Spacegroup H3

SCALA

New developments Corner correction to apply externally calculated correction table as function of detector position Correction table for ESRF ID23-1 Generated from many diffraction patterns (from Chris Nielsen et al.) Maximum correction ≈ 1.4 !

SCALA Automatic optimisation of SD correction parameters BeforeAfter Optimisation of σ’ 2 = SDfac 2 [σ 2 + SdB + (SdAdd ) 2 ] Minimises deviation of Sigma(scatter/σ) from 1 ie flattens out the plot Uses simplex minimisation (thanks to Kevin for code)

SCALA Changed logfile to contain Results section for Baubles Resolution limits for different datasets (in addition to limits by run) Output of multiple datasets to same file: could go into Truncate at the same time (OUTPUT AVERAGE TOGETHER) ROGUEPLOT to plot outliers on detector Minor things Clusters of outliers around tile corners Outliers along rotation axis

SCALA Summary from Baubles Summary table (aka Table 1) LogGraphs

Future developments Pointless & Scala: no major developments planned Bug fixes, respond to complaints, a few small things to change New program, working title AIMLESS Probably eventually all part of same program with POINTLESS, rather than a separate one: name to be chosen! Essentially a rewrite of Scala: work has begun Advantages: More flexibility Possible new scale models time extrapolation detector corrections, cf XDS MODULATION correction Better analysis etc. Assessment of data, advice for user: automatic optimisation of resolution limits, radiation damage vs. completeness, etc, etc

Acknowledgements Ralf Grosse-Kunstleve cctbx Kevin Cowtan clipper, simplex, C++ advice Martyn Winn & CCP4 gang ccp4 libraries Peter Briggs ccp4i Airlie McCoy C++ advice, code etc