Data Reduction using SORTAV Jyväskylä Summer School on Charge Density August 2007 Data Reduction using SORTAV Louis J Farrugia

Data Reduction using SORTAV Jyväskylä Summer School on Charge Density August 2007 Data Reduction using SORTAV SORTAV is a multi-purpose data reduction program, capable of handling data from area-detectors and scintillation point-detectors. It performs the following tasks 1. Time dependent sample decomposition 2. Inter sub-set scaling 3. Empirical absorption correction based on multiple observations 4. Empirical TDS corrections (point-detectors only) 5. Data averaging and merging 6. Bayesian treatment for weak reflections Input data are raw intensities as taken from integration program. For a charge density analysis, we need to have multiple observations (especially with area-detector data) to improve the precision of experimental structure factors. R. H. Blessing (1987) Crystallogr. Rev. 1, 3

Data Reduction using SORTAV Jyväskylä Summer School on Charge Density August 2007 Data Reduction using SORTAV Absorption corrections Numerical – analytical or by Gaussian quadrature Requires measurement of face indices – can be difficult to measure thin plates accurately. If the sample is medium-strongly absorbing, i.e.  > 3 mm-1, then this method is virtually mandatory for highly accurate work. J. de Meulenaar & H. Tompa (1966) Acta Cryst. A19, 1014 P. Coppens, L. Leiserowitz & D. Rabinovich, D. (1965) Acta Cryst. A18 1035. G. T. DeTitta (1985) J. Appl. Cryst. 18, 75 (crystal in a capilliary)

Data Reduction using SORTAV Jyväskylä Summer School on Charge Density August 2007 Data Reduction using SORTAV Absorption corrections Empirical - requires multiple measurements to determine absorption surface. Aaniso = Ah,i for each ith measurement, Ih,i among nh measurements which are equivalent by symmetry (or through azimuthal rotation). The ylm are real spherical harmonics, and their arguments (-uo and u1) are the reversed-incident and diffracted beam unit direction vectors referred to the crystal-based orthonormal axial system. The alm are refinable coefficients obtained by a least squares fit to minimise the residual 2(summed over the equivalent reflections R. H. Blessing (1995) Acta. Cryst. A51, 33

Data Reduction using SORTAV Jyväskylä Summer School on Charge Density August 2007 Data Reduction using SORTAV Data averaging An advanced feature of SORTAV is the outlier downweighting used, which biases the sample mean towards the sample median. The most appropriate for average data sets is the robust resistant Tukey downweighting (which is chosen by default by the SORTAVGUI). The outliers can also be identified and rejected on input – the program will then repeat the process of downweighting. This can be carried on in a cycle until no new outliers are identified. For a high quality data-set, a few cycles suffice to reach this stage R. H. Blessing (1997) J. Appl. Cryst. 30, 421

Data Reduction using SORTAV Jyväskylä Summer School on Charge Density August 2007 Data Reduction using SORTAV Analysis of variance improves the estimate of (F2)

Data Reduction using SORTAV Jyväskylä Summer School on Charge Density August 2007 Data Reduction using SORTAV Rmerge indices Several merging agreement indices are printed, which including adjustments for small sample statistics. normalised mean absolute deviation R1 the normalised RMS deviation R2 and the RMS standardised deviation Z K. Diederichs & P. A. Karplus (1997) Nature Struct Biol. 4, 269

Jyväskylä Summer School on Charge Density August 2007 The SORTAVGUI

Jyväskylä Summer School on Charge Density August 2007 The SORTAVGUI

Jyväskylä Summer School on Charge Density August 2007 The SORTAVGUI

Jyväskylä Summer School on Charge Density August 2007 The SORTAVGUI