##################################################
### Script to specify switches by user

## Definition of variables:
## Ni   = no. of fuel channels
## No   = total no. of channels, fuel (Ni) + detector channels (Nd)
## SDS  = two shutdown system (SDS) exist, SDS1 and SDS2. SDS1 has 3 logical channels with 18 detectors each, SDS2 has 17 detectors instead.
## SR   = centred (sum to 0) ripples
## CPPF = Channel power peaking factor
## SOP  = Channel and flux overpowers
## CCP  = Critical channel powers


#### Enter a value to set the random number seed.
#### Should be a positive integer
seedval= 6789
set.seed(seedval)

##################################################
#### Set Switches
Msec  = 1     # SDS system             (1-SDS1, 2-SDS2)
Mr    = 1     # SR model               (1-direct sampling, 2-variance components, 3-mixture)
Mf    = 1     # CPPF model             (0-deterministic function, 1-multivariate normal)
Mop   = 0     # SOP model              (0-direct sampling, 1-two stages direct sampling, 2-variance components)
Mccp  = 0     # CCP model              (0-direct sampling, 1-two stages direct sampling, 2-variance components)
Mw    = 0     # Weights model for CPPF (0-uniform weights, 1-nonuniform weights) (under development)

Ssoro = 1     # SORO variance          (Ni - 1-Exchangeable, 3-Spatial, 5-Unstructured)
Srfsp = 2     # RFSP variance          (No - 2-Exchangeable, 4-Spatial, 5-Unstructured)
Stuf  = 1     # Tuf variance           (Ni - 1-Exchangeable, 3-Spatial, 5-Unstructured)
Ssop  = 2     # SOP variance           (No - 2-Exchangeable, 4-Spatial, 5-Unstructured)
Sccp  = 1     # CCP variance           (Ni - 1-Exchangeable, 3-Spatial, 5-Unstructured)

## Bias/Variance for SORO
Sfinch=FALSE # Is the evidence from the FINCH industry data to be used to set the variances ?
Sbrfsp=TRUE  # Is the evidence from the VRFSP industry data to be used to set the variances ?

## If Mr = 3, fix the dimension of the mixture model
Ng=3

## Set the prior switch for W parameters: see ./Config/SetEmpiricalBayes.R
## 0 - Empirical Bayes -  will result in hard failure if the set variances is too large.
## 1 - Subjective
## 2 - Vague subjective
prior.switch.SORO = 1
prior.switch.RFSP = 1
prior.switch.TUF  = 1

## Select Tony spec's for Empirical Bayes - This is not used unless corresponding prior.switch =0
prior.switch.SORO.Tony = TRUE
prior.switch.RFSP.Tony = TRUE
prior.switch.TUF.Tony  = FALSE


## Set the parameters for the ripples sets used in the execution
RipS     = 0      #(0-Use full ripple set, 1-Use bounded ripple subset, 2-Use randomly sampled subset)
RipB1    = 1      #(The lower bound ripple for the ripples subset) [RipS=1]
RipB2    = 100    #(The upper bound ripple for the ripples subset) [RipS=1]
RandRips = 500    #(The number of randomly sampled ripples to use) [RipS=2]
RipRep   = FALSE  #(To perform the ripples sampling with (TRUE) or without (FALSE) replacement) [RipS=2]

##################################################
#### Set a Single Perturbation 
#### Enter a value 1-488, or 0 for all perturbations
#### Consult the file Documentation/COPfilelist.csv for the Case number.
Pval=0

##################################################
#### Set Perturbation Group
#### Enter a value 1-10, or 0 for all perturbations (or to select just one).

## 1- APZCDNOM ( 56 cases)
## 2- APZCFNOM ( 28 cases)
## 3- MCANOM   ( 70 cases)
## 4- SCFNOM   ( 22 cases)
## 5- SSNOM    (  8 cases)
## 6- SSSICNOM ( 64 cases)
## 7- SZCDNOM  (112 cases)
## 8- SZCFNOM  ( 56 cases)
## 9- ZCADNOM  ( 48 cases)
##10- ZCAFNOM  ( 24 cases)
Pgroup=0