POINTLESS SCALA Phil Evans POINTLESS What does it

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POINTLESS & SCALA Phil Evans

POINTLESS & SCALA Phil Evans

POINTLESS What does it do? 1. Determination of Laue group & space group from

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 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.

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 21 2 21 ★ I 2 in place of C 2 to give smallest β angle ★

POINTLESS Improved scoring schemes 1. Probability scoring uses Lorentzian distribution (larger tails than Gaussian)

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 63 0. 624 0. 055 0. 856 0. 038 62 0. 695 0. 068 0. 872 0. 039 61 0. 684 0. 248 0. 720 * 0. 703 Correction gives higher peaks, larger probabilities

CCP 4 i interface POINTLESS General options Multiple file input, same dataset Options for

CCP 4 i interface POINTLESS General options Multiple file input, same dataset Options for setting

SCALEPACK example POINTLESS Scalepack files do not include the unit cell, so this must

SCALEPACK example POINTLESS 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 H 3

POINTLESS Consistent indexing to reference file (merged or unmerged) Spacegroup H 3

SCALA

SCALA

SCALA New developments • Corner correction to apply externally calculated correction table as function

SCALA New developments • Corner correction to apply externally calculated correction table as function of detector position Correction table for ESRF ID 23 -1 Generated from many diffraction patterns (from Chris Nielsen et al. )correction ≈ 1. 4 ! Maximum

SCALA • Automatic optimisation of SD correction parameters Before After Optimisation of σ’ 2

SCALA • Automatic optimisation of SD correction parameters Before After Optimisation of σ’ 2 = SDfac 2 [σ2 + Sd. B <Ih> + (Sd. Add <Ih>)2] Minimises deviation of Sigma(scatter/σ) from 1 ie flattens out the plot Uses simplex minimisation (thanks to Kevin for code)

SCALA Minor things • Changed logfile to contain Results section for Baubles • Resolution

SCALA Minor things • 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 Outliers along rotation axis Clusters of outliers around tile corners

SCALA Summary from Baubles Summary table (aka Table 1) Log. Graphs

SCALA Summary from Baubles Summary table (aka Table 1) Log. Graphs

Future developments Pointless & Scala: no major developments planned Bug fixes, respond to complaints,

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

Acknowledgements Ralf Grosse-Kunstleve cctbx Kevin Cowtan clipper, simplex, C++ advice Martyn Winn & CCP

Acknowledgements Ralf Grosse-Kunstleve cctbx Kevin Cowtan clipper, simplex, C++ advice Martyn Winn & CCP 4 gang ccp 4 libraries Peter Briggs ccp 4 i Airlie Mc. Coy C++ advice, code etc