# Retrieved Data Check
Since startR has not been fully stable with many developments going on,
it is important to check manually if the retrieving data array is as you expect before you go for further analysis, especially when several 
parameters are used.

Here we list some tips recommended to pay attention, and some tools for data comparison.

## Index
- Tips  
 (a) [Small dataset](inst/doc/data_check.md#a-small-dataset)  
 (b) [Dimensions](inst/doc/data_check.md#b-dimensions)   
 (c) [Attributes](inst/doc/data_check.md#c-attributes)   
 (d) [Summary](inst/doc/data_check.md#d-summary)  
 (e) [Individual values](inst/doc/data_check.md#e-individual-values) 

- Data retrieval tools  
 (1) [s2dv::Load](inst/doc/data_check.md#1-s2dvload)  
 (2) [ncdf4](inst/doc/data_check.md#2-ncdf4)   
 (3) [easyNCDF](inst/doc/data_check.md#3-easyncdf)   
 (4) [ncview](inst/doc/data_check.md#4-ncview)  

- Extra examination  
 (5) [Compute()](inst/doc/data_check.md#5-compute)   


## Tips  
Here is the general idea. See the [Tools](#data-retrieval-tools) section for example code.

### (a) Small dataset  
It is recommended to use small datasets as the testing sample. For example, reducing 'ensemble' dimension since it is a less-trouble one. 
Shortening 'time' dimension to the length that is light-weighted but can still be representative. 
Take the monthly file of daily data as an example, it is better to include both 30-day and 31-day months. 
As for latitude and longitude, keep both the negative and positive values, 
and use the same way (i.e., values(), indices(), or 'all') to define the selectors.

### (b) Dimensions
Dimension is the most basic thing to check if the data structure is align with 
your expectation. You can check it with or without retrieving the data.

### (c) Attributes  
Check longitude, latitude, and time, etc. Even the dimension length is correct, 
the order may be opposite or out of range. You can check it with or without retrieving the data.
To learn how to get the proper attributes 
by Start(), see [FAQ How-to-#16](/inst/doc/faq.md#16-use-parameter-return_vars-in-start).

### (d) Summary
After checking the data structure, you can look into the values to see if the data 
are retrieved correctly and filled in the right place. First of all, summary 
(mean, min, max, etc.) gives you a general view of the values. If the summary is 
consistent, you know Start() finds the correct files and data position.

### (e) Individual values
There is a chance that the dimensions are mixed, or a few data points are incorrect due to interpolation or other reasons. 
By the overall summary, these mistakes may not be detected. Therefore, checking 
a few data points is recommended. 
It is better to check not only the first element in each dimension but also some points in the middle of the array.


## Data retrieval tools
### (1) s2dv::Load
If you used Load() before, it is convenient to compare with its output directly. 
It is useful when transformation is applied. Other methods provided here can only show original data. 
Also, it helps check the retrieved data structure, not only the values.

```r
# startR
library(startR)

obs_path <- '/esarchive/recon/ecmwf/era5/monthly_mean/$var$_f1h/$var$_$sdate$.nc'
var_name <- 'sfcWind'
sdate <- '201811'
lons.min <- 10
lons.max <- 20
lats.min <- 0
lats.max <- 10

obs1 <- Start(dat = obs_path,
              var = var_name,
              sdate = sdate,
              time = 'all',
              latitude = values(list(lats.min, lats.max)),
              latitude_reorder = Sort(decreasing = T),
              longitude = values(list(lons.min, lons.max)),
              longitude_reorder = CircularSort(0, 360),
              synonims = list(longitude = c('lon', 'longitude'),
                              latitude = c('lat', 'latitude')),
              transform = CDORemapper,
              transform_extra_cells = 2,
              transform_params = list(grid = 'r360x181',
                                      method = 'conservative',
                                      crop = c(lons.min, lons.max, lats.min, lats.max)),
              transform_vars = c('latitude', 'longitude'),
              return_vars = list(time = NULL,
                                 latitude = 'dat',
                                 longitude = 'dat'),
              retrieve = T)

# Load()
library(s2dv)
pobs <- paste0('/esarchive/recon/ecmwf/era5/monthly_mean/',
               '$VAR_NAME$_f1h/$VAR_NAME$_$YEAR$$MONTH$.nc')

obs2 <- Load(var = var_name,
             obs = list(list(path = pobs)),
             sdates = '20181101',
             leadtimemin = 1,
             leadtimemax = 1,
             output = 'lonlat',
             grid = 'r360x181',
             method = 'conservative',
             storefreq = 'monthly',
             latmin = lats.min,
             latmax = lats.max,
             lonmin = lons.min,
             lonmax = lons.max)

# Check
## (a) dimensions
dim(obs1)
#      dat       var     sdate      time  latitude longitude 
#        1         1         1         1        11        11 
dim(obs2$obs)
#  dataset  member   sdate   ftime     lat     lon 
#        1       1       1       1      11      11 

## (b) attributes
attr(obs1, 'Variables')$dat1$longitude
# [1] 10 11 12 13 14 15 16 17 18 19 20
as.vector(obs2$lon)
# [1] 10 11 12 13 14 15 16 17 18 19 20
attr(obs1, 'Variables')$dat1$latitude
# [1] 10  9  8  7  6  5  4  3  2  1  0
as.vector(obs2$lat)
# [1] 10  9  8  7  6  5  4  3  2  1  0

## (c) summary
diff <- drop(obs1) - drop(obs2$obs)
range(diff)
#[1] -4.965553e-05  4.929314e-05
summary(obs1)
#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#  1.090   1.313   1.479   1.636   1.936   2.803 
summary(obs2$obs)
#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#  1.090   1.313   1.479   1.636   1.936   2.803 

## (d) individual values
obs1[1, 1, 1, 1, 1:3, 1:2]
#         [,1]     [,2]
#[1,] 2.345519 2.365326
#[2,] 2.219121 1.958319
#[3,] 1.840537 1.386617
obs2$obs[1, 1, 1, 1, 1:3, 1:2]
#       [,1]   [,2]
#[1,] 2.3455 2.3653
#[2,] 2.2191 1.9583
#[3,] 1.8405 1.3866

```

### (2) ncdf4
It shows original data values in netCDF files. You can get the same domain if the 
interpolation is not applied in Start(), while the data structure will be different.  
It is resource-consuming if the whole domain is retrieved through ncdf4.
If doing so, it is better to use fatnodes/Power9/Nord3 interactive session for more memory space. 
Check [ncdf4 documentation](https://cran.r-project.org/web/packages/ncdf4/index.html) to learn how to use it.

```r
# startR
library(startR)
obs_path <- '/esarchive/recon/ecmwf/era5/monthly_mean/$var$_f1h/$var$_$sdate$.nc'
var_name <- 'sfcWind'
sdate <- '201811'

obs1 <- Start(dat = obs_path,
              var = var_name,
              sdate = sdate,
              time = 'all',
              latitude = indices(1:10),
              longitude = indices(1:10),
              synonims = list(longitude = c('lon', 'longitude'),
                              latitude = c('lat', 'latitude')),
              return_vars = list(time = 'sdate',
                                 latitude = 'dat',
                                 longitude = 'dat'),
              retrieve = T)

# ncdf4
library(ncdf4)
file <- nc_open('/esarchive/recon/ecmwf/era5/monthly_mean/sfcWind_f1h/sfcWind_201811.nc')
obs2 <- ncvar_get(file, 'sfcWind', start = c(1, 1, 1), count = c(10, 10, 1))
nc_close(file)

# Check
## (a) dimensions
dim(obs1)
#      dat       var     sdate      time  latitude longitude 
#        1         1         1         1        10        10 
dim(obs2)
#[1] 10 10

## (c) summary
summary(obs1)
#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#  4.934   4.954   5.025   5.035   5.105   5.165 
summary(as.vector(obs2))
#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#  4.934   4.954   5.025   5.035   5.105   5.165

## (d) individual values
obs1[1, 1, 1, 1, , 2]
# [1] 4.960041 4.959622 4.956322 4.939547 4.991117 5.075977 5.143179 5.157578
# [9] 5.095344 5.051348
obs2[2, ]
# [1] 4.960041 4.959622 4.956322 4.939547 4.991117 5.075977 5.143179 5.157578
# [9] 5.095344 5.051348
```

### (3) easyNCDF  
It is a wrapper package of ncdf4. The idea is similar to ncdf4 but with some more-friendly feature. 
Check [easyNCDF documentation](https://cran.r-project.org/web/packages/easyNCDF/index.html) to learn how to use it.


```r
# startR
# Use the same one as (2).

# easyNCDF
library(easyNCDF)
file <- NcOpen('/esarchive/recon/ecmwf/era5/monthly_mean/sfcWind_f1h/sfcWind_201811.nc')

obs2 <- NcToArray(file, vars_to_read = 'sfcWind',
                  dim_indices = list(longitude = c(1:10),
                                     latitude = c(1:10),
                                     time = c(1)))
NcClose(file)

# Check
## (a) dimensions
dim(obs1)
#      dat       var     sdate      time  latitude longitude 
#        1         1         1         1        10        10 
dim(obs2)
#      var longitude  latitude      time 
#        1        10        10         1 

## (c) summary
summary(obs1)
#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#  4.934   4.954   5.025   5.035   5.105   5.165 
summary(as.vector(obs2))
#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#  4.934   4.954   5.025   5.035   5.105   5.165 

## (d) individual values
obs1[1, 1, 1, 1, , 2]
# [1] 4.960041 4.959622 4.956322 4.939547 4.991117 5.075977 5.143179 5.157578
# [9] 5.095344 5.051348
obs2[1, 2, , 1]
# [1] 4.960041 4.959622 4.956322 4.939547 4.991117 5.075977 5.143179 5.157578
# [9] 5.095344 5.051348
```

### (4) ncview
Using ncview to check netCDF file is the easiest way. While it may be slow. 
It is convenient if you want to check with the map.

In the terminal, type 'ncview <file_name>'. Then check the values dynamically in the pop-up window.
You can also plot the map to compare with ncview. It is more clear if you use a bigger domain (rather than the example here, which only has 10*10 points).

```r
s2dv::PlotEquiMap(obs1[1,1,1,1,,],
                  lon = attributes(obs1)$Variable$dat1$longitude,
                  lat = attributes(obs1)$Variable$dat1$latitude)
```


## Extra examination
### (5) Compute()
Some problems hide in Compute() but not Start(). If your final goal is to use Compute()
for analysis, you can do some simple/dumb tests to ensure the data remain the same
after going through the workflow. 
You can compare the result of Compute() with what you get from Start(retrieve = TRUE), or with any tool mentioned above.

```r
library(startR)
obs_path <- '/esarchive/recon/ecmwf/era5/monthly_mean/$var$_f1h/$var$_$sdate$.nc'
var_name <- 'sfcWind'
sdate <- '201811'
lons.min <- 10
lons.max <- 20
lats.min <- 0
lats.max <- 10

# startR (retrieve = TRUE)
obs1 <- Start(dat = obs_path,
              var = var_name,
              sdate = sdate,
              time = 'all',
              latitude = values(list(lats.min, lats.max)),
              latitude_reorder = Sort(decreasing = T),
              longitude = values(list(lons.min, lons.max)),
              longitude_reorder = CircularSort(0, 360),
              synonims = list(longitude = c('lon', 'longitude'),
                              latitude = c('lat', 'latitude')),
              transform = CDORemapper,
              transform_extra_cells = 2,
              transform_params = list(grid = 'r360x181',
                                      method = 'conservative',
                                      crop = c(lons.min, lons.max, lats.min, lats.max)),
              transform_vars = c('latitude', 'longitude'),
              return_vars = list(time = NULL,
                                 latitude = 'dat',
                                 longitude = 'dat'),
              retrieve = T)


# startR (retrieve = FALSE, use the whole workflow)
obs2 <- Start(dat = obs_path,
              var = var_name,
              sdate = sdate,
              time = 'all',
              latitude = values(list(lats.min, lats.max)),
              latitude_reorder = Sort(decreasing = T),
              longitude = values(list(lons.min, lons.max)),
              longitude_reorder = CircularSort(0, 360),
              synonims = list(longitude = c('lon', 'longitude'),
                              latitude = c('lat', 'latitude')),
              transform = CDORemapper,
              transform_extra_cells = 2,
              transform_params = list(grid = 'r360x181',
                                      method = 'conservative',
                                      crop = c(lons.min, lons.max, lats.min, lats.max)),
              transform_vars = c('latitude', 'longitude'),
              return_vars = list(time = NULL,
                                 latitude = 'dat',
                                 longitude = 'dat'),
              retrieve = F)

func <- function(x) {
  return(x)
}

step <- Step(func,
             target_dims = 'latitude',
             output_dims = 'latitude')

wf <- AddStep(obs2, step)

res <- Compute(wf, chunks = list(time = 1))$output1

# Check
## (a) dimensions
dim(obs1)
#      dat       var     sdate      time  latitude longitude 
#        1         1         1         1        11        11 
dim(res)
# latitude       dat       var     sdate      time longitude 
#       11         1         1         1         1        11 

## (c) summary
diff <- drop(obs1) - drop(res)
range(diff)
#[1] 0 0
summary(obs1)
#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#  1.090   1.313   1.479   1.636   1.936   2.803 
summary(res)
#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#  1.090   1.313   1.479   1.636   1.936   2.803 

## (d) individual values
obs1[1, 1, 1, 1, 1:3, 1:2]
#         [,1]     [,2]
#[1,] 2.345519 2.365326
#[2,] 2.219121 1.958319
#[3,] 1.840537 1.386617
res[1:3, 1, 1, 1, 1, 1:2]
#         [,1]     [,2]
#[1,] 2.345519 2.365326
#[2,] 2.219121 1.958319
#[3,] 1.840537 1.386617

```



