# FAQs

This document intends to be the first reference for any doubts that you may have regarding startR. If you do not find the information you need, please open an issue for your problem.

## Index
1. **How to**
   1. [Choose the number of chunks/jobs/cores in Compute()](#1-choose-the-number-of-chunksjobscores-in-compute)  
   2. [Merge/Reorder dimension in Start() (using parameter 'xxx_across' and 'merge_across_dims')](#2-mergereorder-dimension-in-start-using-parameter-xxx_across-and-merge_across_dims)
   3. [Use self-defined function in Compute()](#3-use-self-defined-function-in-compute)
   4. [Use package function in Compute()](#4-use-package-function-in-compute)
   5. [Do interpolation in Start() (using parameter 'transform')](#5-do-interpolation-in-start-using-parameter-transform)
   6. [Get data attributes without retrieving data to workstation](#6-get-data-attributes-without-retrieving-data-to-workstation)
</b>

2. **Something goes wrong...**
   1. [No space left on device](#1-no-space-left-on-device)
   2. [ecFlow UI remains blue and does not update status](#2-ecflow-ui-remains-blue-and-does-not-update-status)  
   3. [Compute() successfully but then killed on R session](#3-compute-successfully-but-then-killed-on-r-session) 


## 1. How to

### 1. Choose the number of chunks/jobs/cores in Compute()
Run Start() call to see the total size of the data you read in (remember to set ´retrieve = FALSE´).  

Divide data into chunks according to the size of machine memory module (Power9 is 32GB; MN4 is 8GB). The data size per chunk should be 1/3 to 1/2 of the total memory module.  

Find more details in practical_guide.md [How to choose the number of chunks, jobs and cores](inst/doc/practical_guide.md#how-to-choose-the-number-of-chunks-jobs-and-cores)

### 2. Merge/Reorder dimension in Start() (using parameter 'xxx_across' and 'merge_across_dims')
The parameter `'xxx_across = yyy'` indicates that the inner dimension 'xxx' is continuous along the file dimension 'yyy'. A common  example is 'time_across = chunk', when the experiment runs through many years and the result is saved in several chunk files. Find more details in startR documentation.  

If you define this parameter, you can specify 'xxx' with the indices throughout the whole 'yyy' files, not only within one file. See Example 1 below, 'time = indices(1:24)' is available when 'time_across = chunk' is specified. If not, 'time' can only be 12 for most.

One example making advantage of 'xxx_across' is extracting an climate event across years, like El Niño. If the event starts from Nov 2014 to May 2016 (19 months in total), simply specify 'time = indices(11:29)' (Example 2)

The thing you should bear in mind when using this parameter is the returned data structure. First, **the length of the return xxx dimension is the length of the longest xxx in all files**. Take the El Niño above as an example. The first chunk has 2 months, the second chunk has 12 months, and the third chunk has 5 months. Therefore, the length of time dimension will be 12, and the length of chunk dimension will be 3. 

Second, the way Start() store data is **put data at the left-most position**. Take the El Niño (Example 2) above as an example again. The first chunk has only 2 months, so position 1 and 2 have values (which are Nov and Dec 2014). The second chunk has 12 months, so all positions have values (Jan to Dec 2015), while position 3 to 12 will be NA. The third chunk has 5 months, so position 1 to 5 have values (which are Jan to May 2016), while position 6 to 12 will be NA.

It seems more reasonable to put NA at position 1 to 10 in first chunk (Jan to Oct 2014) and and position 6 to 12 in the third chunk (June to Dec 2016). But if the data is not continuous or picked irregularly , it is hard to judge the correct NA position (see Example 3).

Since Start() is very flexible with any possible way to read-in data, it is difficult to include all the possibilities and make the output data structure reasonable all the time. Therefore, it is recommended to understand the way Start() rolls first, then you know what you should expect from the output and will not get confused with what it returns to you.

As for parameter 'merge_across_dims', it decides whether to connect all 'xxx' together along 'yyy' or not. See Example 1. If 'merge_across_dims = TRUE', the chunk dimension will disappear. 'merge_across_dims' simply attaches data one after another, so the NA values (if exist) will be the same places as the unmerged one (see Example 2).

Example 1

```r
data <- Start(dat = repos,
              var = 'tas',
              time = indices(1:24), # each file has 12 months; read 24 months in total
              chunk = indices(1:2),  #two years, each with 12 months
              lat = 'all',
              lon = 'all',
              time_across = 'chunk',
              merge_across_dims = FALSE,  #TRUE,
              return_vars = list(lat = NULL, lon = NULL),
              retrieve = TRUE)
 
#return dimension (merge_across_dims = FALSE)
dat  var time chunk  lat  lon
   1    1   12    2  256  512 
 
#return dimension (merge_across_dims = TRUE)
dat  var time  lat  lon
   1    1   24  256  512 
```

Example 2: El Niño event

```r
repos <- '/esarchive/exp/ecearth/a1tr/cmorfiles/CMIP/EC-Earth-Consortium/EC-Earth3/historical/$memb$/Omon/$var$/gr/v20190312/$var$_Omon_EC-Earth3_historical_$memb$_gr_$chunk$.nc'

data <- Start(dat = repos,
              var = 'tos',
              memb = 'r24i1p1f1',
              time = indices(4:27), # Apr 1957 to Mar 1959
              chunk = c('195701-195712', '195801-195812', '195901-195912'),  
              lat = 'all',
              lon = 'all',
              time_across = 'chunk',
              merge_across_dims = FALSE,
              return_vars = list(lat = NULL, lon = NULL),
              retrieve = TRUE)

> dim(data)
  dat   var  memb  time chunk   lat   lon 
    1     1     1    12     3   256   512 

> data[1,1,1,,,100,100]
          [,1]     [,2]     [,3]
 [1,] 300.7398 300.7659 301.7128
 [2,] 299.6569 301.8241 301.4781
 [3,] 298.3954 301.6472 301.3807
 [4,] 297.1931 301.0621       NA
 [5,] 295.9608 299.1324       NA
 [6,] 295.4735 297.4028       NA
 [7,] 295.8538 296.1619       NA
 [8,] 297.9998 295.2794       NA
 [9,] 299.4571 295.0474       NA
[10,]       NA 295.4571       NA
[11,]       NA 296.8002       NA
[12,]       NA 299.0254       NA

#To move the NAs in the first year to Jan to Mar
> asd <- Subset(data, c(5), list(1))
> qwe <- asd[, , , c(10:12, 1:9), , ,]
> data[, , , , 1, ,] <- qwe

> data[1, 1, 1, , , 100, 100]
          [,1]     [,2]     [,3]
 [1,]       NA 300.7659 301.7128
 [2,]       NA 301.8241 301.4781
 [3,]       NA 301.6472 301.3807
 [4,] 300.7398 301.0621       NA
 [5,] 299.6569 299.1324       NA
 [6,] 298.3954 297.4028       NA
 [7,] 297.1931 296.1619       NA
 [8,] 295.9608 295.2794       NA
 [9,] 295.4735 295.0474       NA
[10,] 295.8538 295.4571       NA
[11,] 297.9998 296.8002       NA
[12,] 299.4571 299.0254       NA

```

Example 3: Read in three winters (DJF)

```r
repos <- '/esarchive/exp/ecearth/a1tr/cmorfiles/CMIP/EC-Earth-Consortium/EC-Earth3/historical/$memb$/Omon/$var$/gr/v20190312/$var$_Omon_EC-Earth3_historical_$memb$_gr_$chunk$.nc'

data <- Start(dat = repos,
              var = 'tos',
              memb = 'r24i1p1f1',
              time = c(12:14, 24:26, 36:38), # DJF, Dec 1999 to Jan 2002
              chunk = c('199901-199912', '200001-200012', '200101-200112', '200201-200212'),  
              lat = 'all',
              lon = 'all',
              time_across = 'chunk',
              merge_across_dims = TRUE,
              return_vars = list(lat = NULL, lon = NULL),
              retrieve = TRUE)

> dim(data)
 dat  var memb time  lat  lon 
   1    1    1   12  256  512 

> data[1, 1, 1, , 100, 100]
 [1] 300.0381       NA       NA 301.3340 302.0320 300.3575 301.0930 301.4149
 [9] 299.3486 300.7203 301.6695       NA


#Remove NAs and rearrange DJF
> qwe <- Subset(asd, c(4), list(c(1, 4:11)))
> zxc <- InsertDim(InsertDim(qwe, 5, 3), 6, 3)
> zxc <- Subset(zxc, 'time', list(1), drop = 'selected')
> zxc[, , , 1:3, 1, ,] <- qwe[, , , 1:3, ,]
> zxc[, , , 1:3, 2, ,] <- qwe[, , , 4:6, ,]
> zxc[, , , 1:3, 3, ,] <- qwe[, , , 7:9, ,]
> names(dim(zxc))[4] <- c('month')
> names(dim(zxc))[5] <- c('year')

> dim(zxc)
  dat   var  memb month  year   lat   lon 
    1     1     1     3     3   256   512 

> zxc[1, 1, 1, , , 100, 100]
         [,1]     [,2]     [,3]
[1,] 300.0381 300.3575 299.3486
[2,] 301.3340 301.0930 300.7203
[3,] 302.0320 301.4149 301.6695

```

### 3. Use self-defined function in Compute()

The workflow to use Compute() is: 'define the function' -> 'use Step() to assign the target/output dimension' -> 'use AddStep() to build up workflow' ->  'use Compute() to launch jobs on either local workstation or fatnodes/Power9'.

It is no problem when you only have a simple function directly defined in your script (like the example in [practical guide](https://earth.bsc.es/gitlab/es/startR/blob/master/inst/doc/practical_guide.md#step-and-addstep)). However, if the function is more complicated, you may want to save it as an independent file. In this case, the machines (Power 9 or fatnodes) cannot recognize your function therefore the jobs will fail (if you use Compute() at your own local workstation, the problem does not exist.)  

The solution is simple. First, put your function file at somewhere in the machine. For example, in Power 9, put own_func.R at `/esarchive/scratch/<your_user_name>`. Second, in the script, source the function in the function definition (see the example below). Hence, the machine can find your function.  

```r
data <- Start(...,
              retrieve = FALSE)

func <- function(x) {
    source("/esarchive/scratch/aho/own_func.R")  #the path in Power 9
    y <- own_func(x, posdim = 'time')
    return(y)
}

step <- Step(fun = func,
             target_dims = c('time'),
             output_dims = c('time'))#,

wf <- AddStep(data, step)

res <- Compute(wf, ...)

```

### 4. Use package function in Compute()

In the workflow for Compute(), first step is to define the function. If you want to use the function in certain R package, you need to check if the package is involved in the R module (`r_module`) or library (`lib_dir`). Then, specify the package name before the function name (see example below) so the machine can recognize which function you refer to.

```r
data <- Start(...,
              retrieve = FALSE)

func <- function(x) {
    y <- s2dverification::Season(x, posdim = 'time')  #specify package name
    return(y)
}

step <- Step(fun = func,
             target_dims = c('time'),
             output_dims = c('time'))

wf <- AddStep(data, step)

  res <- Compute(wf,
                 chunks = list(latitude = 2,
                               longitude = 2),
                 threads_load = 2,
                 threads_compute = 4,
                 cluster = list(queue_host = 'p1',  #your alias for power9
                                queue_type = 'slurm',
                                temp_dir = '/gpfs/scratch/bsc32/bsc32734/startR_hpc/',
                                lib_dir = '/gpfs/projects/bsc32/share/R_libs/3.5/',  #s2dverification is involved here, so the machine can find Season()
                                r_module = 'startR/0.1.2-foss-2018b-R-3.5.0',
                                job_wallclock = '00:10:00',
                                cores_per_job = 4,
                                max_jobs = 4,
                                bidirectional = FALSE,
                                polling_period = 50
                               ),
                 ecflow_suite_dir = '/home/Earth/aho/startR_local/',
                 wait = TRUE
                 )

```

### 5. Do interpolation in Start() (using parameter 'transform')

If you want to do the interpolation within Start(), you can use the following four parameters:  

1.  **`transform`**: Assign the interpolation function. It is recommended to use `startR::CDORemapper`, the wrapper function of s2dverification::CDORemap().  
2.  **`transform_params`**: A list of the required inputs for `transform`. Take `transform = CDORemapper` as an example, the common items are:  
   - `grid`: A character string specifying either a name of a target grid (recognized by CDO, e.g., 'r256x128', 't106grid') or a path to another NetCDF file with the target grid (a single grid must be defined in such file).  
   - `method`: A character string specifying an interpolation method (recognized by CDO, e.g., 'con', 'bil', 'bic', 'dis'). The following long names are also supported: 'conservative', 'bilinear', 'bicubic', and 'distance-weighted'.  
   - `crop`: Whether to crop the data after interpolation with 'cdo sellonlatbox' (TRUE) or to extend interpolated data to the whole region as CDO does by default (FALSE).  
             If crop = TRUE, the longitude and latitude borders to be cropped at are taken as the limits of the cells at the borders ('lons' and 'lats' are perceived as cell centers), i.e., the resulting array will contain data that covers the same area as the input array. This is equivalent to specifying crop = 'preserve', i.e. preserving area.   
             If crop = 'tight', the borders to be cropped at are taken as the minimum and maximum cell centers in ’lons’ and ’lats’, i.e., the area covered by the resulting array may be smaller if interpolating from a coarse grid to a fine grid.  
             The parameter ’crop’ also accepts a numeric vector of custom borders: c(western border, eastern border, southern border, northern border).  

3.  **`transform_vars`**: A character vector of the inner dimensions to be transformed. E.g., c('latitude', 'longitude').  
4.  **`transform_extra_cells`**: A numeric indicating the number of grid cell to extend from the borders if the interpolating region is a subset of the whole region. 2 as default, which is consistent with the method in s2dverification::Load(). 

You can find an example script here [ex1_1_tranform.R](/inst/doc/usecase/ex1_1_tranform.R)  
You can see more information in s2dverification::CDORemap documentation [here](https://earth.bsc.es/gitlab/es/s2dverification/blob/master/man/CDORemap.Rd).


### 6. Get data attributes without retrieving data to workstation

One of the most useful functionalities of Start() is the parameter `retrieve = FALSE`. It creates a pointer to data repository and tells you the data information without occupying your workstation memory. The better thing is, even the data is not actually retrieved, you can still use its attributes:  
```r
  header <- Start(dat = repos,
                  ...,
                  retrieve = FALSE)
  class(header)
#[1] "startR_cube"

# check attributes
  str(attr(header, 'Variables'))
# Get longitude and latitude
  lons <- attr(header, 'Variables')$common$lon
  lats <- attr(header, 'Variables')$common$lat
# Get dimension
  dim <- attr(header, 'Dimensions')

```

And if you want to retrieve the data to the workstation afterward, you can use `eval()`:  
```r
  data <- eval(header)

  class(data)
#[1] "startR_array"

# Get dimension
  dim(data)
```
Find examples at [usecase.md](/inst/doc/usecase.md), ex1_1 and ex1_3.


## Something goes wrong...

### 1. No space left on device

An issue of R is the accumulated trash files, which occupy the machine memory therefore crash R. If the size of data your R script deal with is reasonable but R crashes immediately after running and returns the ERROR:    
>  
> No space left on device  
>
Go to **/dev/shm/** and `rm <large_trash_file_name>` 

Find more discussion in this [issue](https://earth.bsc.es/gitlab/es/s2dverification/issues/221)

### 2. ecFlow UI remains blue and does not update status

This situation will occur if:  
1. The Compute() parameter `wait` is set to be `FALSE`, and  
2. Launch jobs on an HPC where the connection with its login node is unidirectional (e.g., Power 9)  

Under this condition, the ecFlow UI will remain blue and will not update the status. 
To solve this problem, use `Collect()` in the R terminal after running Compute():

```r
  res <- Compute(wf,
                 ...,
                 wait = FALSE)

  result <- Collect(res, wait = TRUE)  #it will update ecflow_ui status continuously, but will block the R session
  result <- Collect(res, wait = FALSE)  #it will return the ecflow_ui status once only, but will not block the R session
```

The last line will block the terminal but meanwhile update the status just like what you see with `wait = TRUE`.

### 3. Compute() successfully but then killed on R session

When Compute() on HPCs, the machines are able to process data which are much larger than the local workstation, so the computation works fine (i.e., on ec-Flow UI, the chunks show yellow in the end.) However, after the computation, the output will be sent back to local workstation. **If the returned data is larger than the available local memory space, your R session will be killed.** Therefore, always pre-check if the returned data will fit in your workstation free memory or not. If not, subset the input data or reduce the output size through more computation.  

Further explanation: though the complete output (i.e., merging all the chunks into one returned array) cannot be sent back to workstation, but the chunking results (.Rds file) are completed and saved in the directory '<ecflow_suite_dir>/STARTR_CHUNKING_<job_id>'. If you still want to use the chunking results, you can find them there.