practical_guide.md

# Practical guide for using startR at BSC

In this guide, some practical examples are shown for you to see how to use startR to process large data sets in parallel on your Earth Sciences department workstation or on the BSC's HPCs. 

In order to do so, you need to understand 4 functions, all of them included in the startR package:
 - Start() --> for declaing the data sets to process
 - Step() and AddStep() --> for specifying the operation to be applied to the data
 - Compute() --> for specifying the HPC to be employed, the number of chunks and cores, and to trigger the computation

But, in first place, you must follow the deployment steps to make sure startR will work with the HPC of your choice, and follow some tricks for a better experience.

## Deployment at BSC

The full deployment steps are detailed in the [**Deployment**](inst/doc/deployment.md) section. However at BSC you do not need to follow them since everything is already installed for you. You just need to set up passwordless access:
1- generate an ssh pair of keys if you do not have one, using `ssh-keygen -t rsa`
2- ssh to the HPC login node and create a directory where to store it, using `ssh username@hostname_or_ip mkdir -p .ssh`
3- dump your public key on a new file under that folder, using `cat .ssh/id_rsa.pub | ssh username@hostname_or_ip 'cat >> .ssh/authorized_keys'`
4- adjust the permissions, using `ssh username@hostname_or_ip "chmod 700 .ssh; chmod 640 .ssh/authorized_keys"`
5- if your username is different on your workstation and on the HPC login node, add an entry in the file .ssh/config in your workstation as follows:
```
  Host short_name_of_the_host
    HostName hostname_or_ip
    User username
    IdentityFile ~/.ssh/id_rsa
```

You are almost good to go. Do not forget adding the following lines on your .bashrc on CTE-Power, if you are planning to run on CTE-Power:
```
if [[ $BSC_MACHINE == "power" ]] ; then
  module unuse /apps/modules/modulefiles/applications
  module use /gpfs/projects/bsc32/software/rhel/7.4/ppc64le/POWER9/modules/all/
fi
```

Also, you can add the following lines on your .bashrc on your workstation for convenience:
```
alias ctp='ssh -X username@p9login1.bsc.es'
alias start='module load R CDO ecFlow'
```

Then, when you open a new terminal session, you will just need to run the following commands and a fresh R session will pop up with the startR environment ready to use.
```
start
R
```

## Start()

In order to declare the data sets you want to process, you first need to specify a special path that shows where all the involved NetCDF files you want to process are stored, containing some wildcards in those parts of the path that vary across files. This special path is also called "path pattern".

Before defining an example path pattern, let's introduce some target NetCDF files. In esarchive, we can find the following files:

```
/esarchive/exp/ecmwf/system5_m1/6hourly/
  |--tas/
  |   |--tas_19930101.nc
  |   |--tas_19930201.nc
  |   |        ...
  |   |--tas_20171201.nc
  |--tos/
      |--tos_19930101.nc
      |--tos_19930201.nc
      |        ...
      |--tos_20171201.nc
```

A path pattern that could be used to define the location of these files in a compact way is the following:

```r
repos <- '/esarchive/exp/ecmwf/system5_m1/6hourly/$var$/$var$_$sdate$.nc'
```

The names of the wildcards used (the pieces wrapped between '$' symbols) can be given any names.

Once the path pattern is specified, a Start() call can be built, requesting the values of interest for each of the wildcards (also called outer dimensions), as well as for each of the dimensions inside the NetCDF files (inner dimensions).

You can check in advance which dimensions are inside the NetCDF files by checking one of them with the basic NetCDF tools:

```
ncdump -h /esarchive/exp/ecmwf/system5_m1/6hourly/tas/tas_19930101.nc
```

This would REVELAR the following inner dimensions: 'ensemble', 'time', 'latitude', and 'longitude'.

We can now put the Start call together:

```r
data <- Start(dat = repos,
              # outer dimensions
              var = 'tas',
              sdate = '19930101',
              # inner dimensions
              ensemble = 'all',
              time = 'all',
              latitude = 'all',
              longitude = 'all')
```

This will yield some output messages:

```r
* Exploring files... This will take a variable amount of time depending
*   on the issued request and the performance of the file server...
* Detected dimension sizes:
*         dat:    1
*         var:    1
*       sdate:    1
*    ensemble:   25
*        time:  860
*    latitude:  640
*   longitude: 1296
* Total size of involved data:
*   1 x 1 x 1 x 25 x 860 x 640 x 1296 x 8 bytes = 132.9 Gb
* Successfully discovered data dimensions.
Warning messages:
1: ! Warning: Parameter 'pattern_dims' not specified. Taking the first dimension,
!   'dat' as 'pattern_dims'. 
2: ! Warning: Could not find any pattern dim with explicit data set descriptions (in
!   the form of list of lists). Taking the first pattern dim, 'dat', as
!   dimension with pattern specifications.
```

The warnings shown are normal, and could be avoided with a more wordy specification of the parameters to the Start function.

The dimensions of the selected data set and the total size are shown.

As you will notice, this Start call is very fast, even though several GB of data are involved. This is because Start is simply discovering the location and dimension of the involved data. You can give a quick look to the collected metadata with `str(data)`.

```r
Class 'startR_header' length 9 Start(dat = "/esarchive/exp/ecmwf/system5_m1/6hourly/$var$/$var$_$sdate$.nc",      var = "tas", sdate = "19930101", ensemble = "all", time = "all", latitude = "all",  ...
  ..- attr(*, "Dimensions")= Named num [1:7] 1 1 1 25 860 ...
  .. ..- attr(*, "names")= chr [1:7] "dat" "var" "sdate" "ensemble" ...
  ..- attr(*, "Variables")=List of 2
  .. ..$ common: NULL
  .. ..$ dat1  : NULL
  ..- attr(*, "ExpectedFiles")= chr [1, 1, 1] "/esarchive/exp/ecmwf/system5_m1/6hourly/tas/tas_19930101.nc"
  ..- attr(*, "FileSelectors")=List of 1
  .. ..$ dat1:List of 3
  .. .. ..$ dat  :List of 1
  .. .. .. ..$ : chr "dat1"
  .. .. ..$ var  :List of 1
  .. .. .. ..$ : chr "tas"
  .. .. ..$ sdate:List of 1
  .. .. .. ..$ : chr "19930101"
  ..- attr(*, "PatternDim")= chr "dat"
```

There are no constrains for the numer or names of the outer or inner dimensions. In other words, Start will handle NetCDF files with any number of dimensions with any name, as well as files distributed in complex ways, since you can use customized wildcards in the path pattern.

If you are interested in actually loading the entire data set in your machine *(be careful!)* you can do so in two ways:
- adding the parameter `retrieve = TRUE` in your Start call.
- evaluating the object returned by Start: `data_load <- eval(data)`

You may realize that this functionality is similar to the `Load()` function in the s2dverification package. In fact, `Start()` is more advanced and flexible, although `Load()` is more mature and consistent for loading classic seasonal to decadal forecasting data. `Load()` will be adapted in the future to use `Start()` internally.

As you can see in the Start call we issued, we have requested specific values for the outer dimensions (e.g. `var = 'tas'` or `sdate = '19930101'`), but vectors of multiple values, numeric indices, or keywords can be used. For example, `var = c('tas', 'tos')`, `sdate = 1:5` or `sdate = 'all'`. See the documentation on the Start function on GitLab (https://earth.bsc.es/gitlab/es/startR/blob/master/vignettes/start.md) or in `?Start` for more information.

## Step() and AddStep()

Once the data sources are declared, we can define the operation to be applied. The operation needs to be encapsulated in the form of an R function receiving one or more multidimensional arrays (plus additional helper parameters) and returning one or more multidimensional arrays. For example:

```r
fun <- function(x) {
  r <- sqrt(sum(x ^ 2) / length(x))
  for (i in 1:100) {
    r <- sqrt(sum(x ^ 2) / length(x))
  }
  dim(r) <- c(time = 1)
  r
}
```

Then, the startR Step for this operation can be defined with the function `Step`, which required for a proper functioning to specify the names of the dimensions of the input arrays expected by the function (in this example, a single array with the dimensions 'ensemble' and 'time'), as well as the names of the dimensions the function returns:

```r
step <- Step(fun = fun, 
             target_dims = c('ensemble', 'time'), 
             output_dims = c('time'))
```

Finally, a workflow of steps can be assembled as follows:

```r
wf <- AddStep(data, step)
```

If multiple data sources were to be provided to a step, they could be provided as a list.

It is not possible for now to define workflows with more than one step. This is pending future work.

what about defining library(blabla) in the code of the function? how to deal with that?


## Compute() locally

Once the data sources are declared and the workflow is defined, we can proceed to specify the execution parameters (including which platform to run on) and trigger the execution.

required ecFlow?
required CDO?

```r
res <- Compute(wf,
               chunks = list(latitude = 2,
                             longitude = 2),
               threads_load = 1,
               threads_compute = 2,
               #cluster = list(queue_host = 'p9login1.bsc.es',
               #               queue_type = 'slurm',
               #               data_dir = '/gpfs/projects/bsc32/share/startR_data_repos/gpfs/archive/bsc32/',
               #               temp_dir = '/gpfs/scratch/bsc32/bsc32473/startR_tests/',
               #               lib_dir = '/gpfs/projects/bsc32/share/R_libs/3.5/',
               #               #init_commands = list('module load intel/16.0.1'),
               #               r_module = 'R/3.5.0',
               #               #ecflow_module = 'ecFlow/4.9.0-foss-2015a',
               #               #node_memory = NULL, #not working
               #               cores_per_job = 2,
               #               job_wallclock = '00:10:00',
               #               max_jobs = 4,
               #               extra_queue_params = list('#SBATCH --qos=bsc_es'),
               #               bidirectional = FALSE,
               #               polling_period = 10#,
               #               #special_setup = 'marenostrum4'
               #              ),
               #ecflow_suite_dir = '/home/Earth/nmanuben/test_remove/',
               #ecflow_server = NULL,
               silent = FALSE,
               debug = FALSE,
               wait = FALSE)
```

compute will return a data array, as if it was a variable in your R session

discuss ecFlow

discuss plotProfiling

discuss use of metadata (dates) in the Step

summary of all code done so far:

## Compute() on HPC

setup steps:

having startR installed on workstation and HPC (done)
having Step dependencies on HPC
having passwordless connection (how to?)
having rsync, ssh, ... on all machines
ecflow??
having the data:
- either on a shared file system
- either on remote file systems (rsync)
- either on remote file systems (with special transfer mechanism, mn4)
not required to ssh manually to the HPC

example on power9

```r
library(startR)

#repos <- '/esarchive/exp/ecmwf/system5_m1/6hourly/$var$/$var$_$sdate$.nc'
repos <- '/esarchive/exp/ecmwf/system5_m1/6hourly/$var$-longitudeS1latitudeS1all/$var$_$sdate$.nc'
data <- Start(dat = repos,
              var = 'tas',
              #sdate = 'all',
              sdate = indices(1),
              ensemble = 'all',
              time = 'all',
              #latitude = 'all',
              latitude = indices(1:40),
              #longitude = 'all',
              longitude = indices(1:40),
              retrieve = FALSE)
lons <- attr(data, 'Variables')$common$longitude
lats <- attr(data, 'Variables')$common$latitude

fun <- function(x) apply(x + 1, 2, mean)
step <- Step(fun, c('ensemble', 'time'), c('time'))
wf <- AddStep(data, step)

res <- Compute(wf,
               chunks = list(latitude = 2,
                             longitude = 2),
               threads_load = 1,
               threads_compute = 2,
               cluster = list(queue_host = 'p9login1.bsc.es',
                              queue_type = 'slurm',
                              data_dir = '/gpfs/projects/bsc32/share/startR_data_repos/gpfs/archive/bsc32/',
                              temp_dir = '/gpfs/scratch/bsc32/bsc32473/startR_tests/',
                              lib_dir = '/gpfs/projects/bsc32/share/R_libs/3.5/',
                              #init_commands = list('module load intel/16.0.1'),
                              r_module = 'R/3.5.0-foss-2018b',
                              #ecflow_module = 'ecFlow/4.9.0-foss-2015a',
                              #node_memory = NULL, #not working
                              cores_per_job = 2,
                              job_wallclock = '00:10:00',
                              max_jobs = 4,
                              extra_queue_params = list('#SBATCH --qos=bsc_es'),
                              bidirectional = FALSE,
                              polling_period = 10#,
                              #special_setup = 'marenostrum4'
                             ),
               ecflow_suite_dir = '/home/Earth/nmanuben/test_remove/',
               ecflow_server = NULL,
               silent = FALSE,
               debug = FALSE,
               wait = TRUE)
```

## Example using obs data / or more than one data source

```r
crps <- function(x, y) {
  mean(SpecsVerification::EnsCrps(x, y, R.new = Inf))
}

library(startR)

repos <- '/perm/ms/spesiccf/c3ah/qa4seas/data/seasonal/g1x1/ecmf-system4/msmm/atmos/seas/tprate/12/ecmf-system4_msmm_atmos_seas_sfc_$date$_tprate_g1x1_init12.nc'

data <- Start(dat = repos,
              var = 'tprate',
              date = 'all',
              time = 'all',
              number = 'all',
              latitude = 'all',
              longitude = 'all',
              return_vars = list(time = 'date'))

dates <- attr(data, 'Variables')$common$time

repos <- '/perm/ms/spesiccf/c3ah/qa4seas/data/ecmf-ei_msmm_atmos_seas_sfc_19910101-20161201_t2m_g1x1_init02.nc'

obs <- Start(dat = repos,
             var = 't2m',
             time = values(dates),
             latitude = 'all',
             longitude = 'all',
             split_multiselected_dims = TRUE)

s <- Step(crps, target_dims = list(c('date', 'number'), c('date')),
                output_dims = NULL)
wf <- AddStep(list(data, obs), s)

r <- Compute(wf,
             chunks = list(latitude = 10,
                           longitude = 3),
             cluster = list(queue_host = 'cca',
                            queue_type = 'pbs',
                            max_jobs = 10,
                            init_commands = list('module load ecflow'),
                            r_module = 'R/3.3.1',
                            extra_queue_params = list('#PBS -l EC_billing_account=spesiccf')),
             ecflow_output_dir = '/perm/ms/spesiccf/c3ah/startR_test/',
             is_ecflow_output_dir_shared = FALSE
            )
```

```r
repos <- paste0('/esnas/exp/ecmwf/system4_m1/6hourly/',
                '$var$/$var$_$sdate$.nc')

system4 <- Start(dat = repos,
                 var = 'sfcWind',
                 #sdate = paste0(1981:2015, '1101'),
                 sdate = paste0(1981:1984, '1101'),
                 #time = indices((30*4+1):(120*4)),
                 time = indices((30*4+1):(30*4+4)),
                 ensemble = 'all',
                 #ensemble = indices(1:6),
                 #latitude = 'all',
                 latitude = indices(1:10),
                 #longitude = 'all',
                 longitude = indices(1:10),
                 return_vars = list(latitude = NULL,
                                    longitude = NULL,
                                    time = c('sdate')))

repos <- paste0('/esnas/recon/ecmwf/erainterim/6hourly/',
                '$var$/$var$_$file_date$.nc')

dates <- attr(system4, 'Variables')$common$time
dates_file <- sort(unique(gsub('-', '', sapply(as.character(dates),
substr, 1, 7))))

erai <-    Start(dat = repos,
                 var = 'sfcWind',
                 file_date = dates_file,
                 time = values(dates),
                 #latitude = 'all',
                 latitude = indices(1:10),
                 #longitude = 'all',
                 longitude = indices(1:10),
                 time_var = 'time',
                 time_tolerance = as.difftime(1, units = 'hours'),
                 time_across = 'file_date',
                 return_vars = list(latitude = NULL,
                                    longitude = NULL,
                                    time = 'file_date'),
                 merge_across_dims = TRUE,
                 split_multiselected_dims = TRUE)

step <- Step(eqmcv_atomic,
             list(a = c('ensemble', 'sdate'),
                  b = c('sdate')),
             list(c = c('ensemble', 'sdate')))

res <- Compute(step, list(system4, erai),
               chunks = list(latitude = 5,
                             longitude = 5,
                             time = 2),
               cluster = list(queue_host = 'bsceslogin01.bsc.es',
                              max_jobs = 4,
                              cores_per_job = 2),
               shared_dir = '/esnas/scratch/nmanuben/test_bychunk',
               wait = FALSE) 
```

## Example on marenostrum 4

```r
library(startR)

#repos <- '/esarchive/exp/ecmwf/system5_m1/6hourly/$var$/$var$_$sdate$.nc'
repos <- '/esarchive/exp/ecmwf/system5_m1/6hourly/$var$-longitudeS1latitudeS1all/$var$_$sdate$.nc'
data <- Start(dat = repos,
              var = 'tas',
              #sdate = 'all',
              sdate = indices(1),
              ensemble = 'all',
              time = 'all',
              #latitude = 'all',
              latitude = indices(1:40),
              #longitude = 'all',
              longitude = indices(1:40),
              retrieve = FALSE)
lons <- attr(data, 'Variables')$common$longitude
lats <- attr(data, 'Variables')$common$latitude

fun <- function(x) apply(x + 1, 2, mean)
step <- Step(fun, c('ensemble', 'time'), c('time'))
wf <- AddStep(data, step)

res <- Compute(wf,
               chunks = list(latitude = 2,
                             longitude = 2),
               threads_load = 1,
               threads_compute = 2,
               cluster = list(queue_host = 'mn2.bsc.es',
                              queue_type = 'slurm',
                              data_dir = '/gpfs/projects/bsc32/share/startR_data_repos/',
                              temp_dir = '/gpfs/scratch/pr1efe00/pr1efe03/startR_tests/',
                              lib_dir = '/gpfs/projects/bsc32/share/R_libs/3.4/',
                              #init_commands = list('module load netcdf/4.4.1.1'),
                              r_module = 'R/3.4.0',
                              #ecflow_module = 'ecFlow/4.9.0-foss-2015a',
                              #node_memory = NULL, #not working
                              cores_per_job = 2,
                              job_wallclock = '00:10:00',
                              max_jobs = 4,
                              extra_queue_params = list('#SBATCH --qos=prace'),
                              bidirectional = FALSE,
                              polling_period = 10,
                              special_setup = 'marenostrum4'
                             ),
               ecflow_suite_dir = '/home/Earth/nmanuben/test_remove/',
               ecflow_server = NULL,
               silent = FALSE,
               debug = FALSE,
               wait = TRUE)
```

## Example on cca