We will assume that the diffraction data has been converted to CNS format using to_cns or CCP4. We will also assume that the twinning operator and twinning fraction has already been identified (see previous section). There will then be a reflection file containing the observed structure factor amplitudes and estimated errors (sigma values). Cross validation will be used to monitor the subsequent refinement of the model and also to reduce bias in map calculations. The cross validation information is added to the reflection file using the CNS task file make_cv_twin.inp.
cns_solve < make_cv_twin.inp > make_cv_twin.out [8 seconds]
It is essential that this task file (and NOT the standard make_cv.inp task file) is used to create the cross-validation set for twinned data. The make_cv_twin.inp task file uses the twinning operator to ensure that pairs of twin related reflections are in the same set (either working or test).
A new reflection array is created, by default the name TEST is used. The array contains integer numbers, by default 1 signifies that the reflection will be used for cross-validation (i.e. excluded from refinement). The output of the make_cv_twin.inp task file is a CNS formatted reflection file. This will contain a header which describes the data items in the file, followed by the data items themselves. The file is plain ASCII and can be viewed with any text viewer/editor. Example header from a CNS reflection file with cross validation information:
NREFlection= 23933 ANOMalous=FALSe { equiv. to HERMitian=TRUE} DECLare NAME=FOBS DOMAin=RECIprocal TYPE=REAL END DECLare NAME=SIGMA DOMAin=RECIprocal TYPE=REAL END DECLare NAME=TEST DOMAin=RECIprocal TYPE=INTE END INDE 0 0 6 FOBS= 433.600 SIGMA= 11.500 TEST= 0 INDE 0 0 12 FOBS= 194.500 SIGMA= 3.400 TEST= 0 INDE 0 0 15 FOBS= 275.600 SIGMA= 7.900 TEST= 0 INDE 0 0 18 FOBS= 354.500 SIGMA= 7.000 TEST= 1 INDE 0 0 21 FOBS= 643.700 SIGMA= 17.300 TEST= 0Script to run this tutorial