POINTLESS & SCALA

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POINTLESS & SCALA Phil Evans

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POINTLESS & SCALA. Phil Evans. POINTLESS. What does it do?. Determination of Laue group & space group from unmerged data Finds highest symmetry lattice consistent with unit cell - PowerPoint PPT Presentation

Transcript of POINTLESS & SCALA

Page 1: POINTLESS & SCALA

POINTLESS & SCALA

Phil Evans

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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 E2, 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)

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POINTLESSNew features

• Input from XDS & Scalepack formatsXDS_ASCII.HKL or INTEGRATE.HKLNote 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

★ I2 in place of C2 to give smallest β angle

• Choose solution from previous run• Exclude batch ranges

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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 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

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POINTLESSCCP4i interface

Multiple file input,

same dataset

Options for setting

General options

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POINTLESSSCALEPACK example

Scalepack files do not include the unit cell, so this must be given

Result displayed by Baubles

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POINTLESSConsistent indexing to reference file(merged or unmerged)Spacegroup H3

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SCALA

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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 !

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SCALA•Automatic optimisation of SD correction parameters

Before After

Optimisation of σ’2 = SDfac2 [σ2 + SdB <Ih> + (SdAdd <Ih>)2]

Minimises deviation of Sigma(scatter/σ) from 1ie flattens out the plotUses simplex minimisation (thanks to Kevin for code)

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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 cornersOutliers along

rotation axis

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SCALA

Summary from Baubles

Summary table (aka Table 1)

LogGraphs

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Future developments

Pointless & Scala: no major developments plannedBug fixes, respond to complaints, a few small things to changeNew program, working title AIMLESSProbably 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

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Acknowledgements

Ralf Grosse-Kunstleve cctbxKevin Cowtan clipper, simplex, C++ adviceMartyn Winn & CCP4 gang ccp4 librariesPeter Briggs ccp4iAirlie McCoy C++ advice, code etc