inst/scripts/99_bathymetry_hybrid_example.R

In jae0/aegis.bathymetry: Bathymetry processing

# Bathymetry


# Spatial interpolation using stmv/carstm hybrid

require(aegis)
require(aegis.bathymetry)
require(sf)
require(stmv)
require(INLA)
inla.setOption(mkl=FALSE) 

## :: main difference is:  project_class="hybrid"  (vs "stmv" above)

p0 = aegis::spatial_parameters( spatial_domain="bathymetry_example",
  aegis_proj4string_planar_km="+proj=utm +ellps=WGS84 +zone=20 +units=km",
  dres=1/60/4, pres=1, lon0=-64, lon1=-62, lat0=44, lat1=45, psignif=2 )

# or:
p0 = stmv_test_data( "aegis.test.parameters")

input = stmv::stmv_test_data( datasource="aegis.space", p=p0)
input = sf::st_as_sf( input, coords=c("lon","lat"), crs=st_crs(projection_proj4string("lonlat_wgs84")) )
input = sf::st_transform( input, crs=st_crs(p0$aegis_proj4string_planar_km) )
input = as.data.frame( cbind( input$z, st_coordinates(input) ) )
names(input) = c("z", "plon", "plat")
input = input[ which(is.finite(input$z)), ]
output = list( LOCS = spatial_grid(p0) )
DATA = list( input = input,  output = output )

input = output = NULL
gc()


p = bathymetry_parameters(
  p=p0,  # start with spatial settings of input data
  project_class="hybrid",
  stmv_model_label="carstm_statsgrid10km",  # this label is used as directory for storage
  data_root = file.path( tempdir(), "bathymetry_example"),
  DATA = DATA,
  spatial_domain = p0$spatial_domain,
  spatial_domain_subareas =NULL,
  pres=1,
  inputdata_spatial_discretization_planar_km = p0$pres/5,  # pres = 0.5 and used for prediction so inputs should be smaller
  dimensionality="space",
  stmv_variables = list(Y="z"),  # required as fft has no formulae
  stmv_global_modelengine = "none",  # too much data to use glm as an entry into link space ... use a direct transformation
  stmv_local_modelengine="carstm",
  stmv_local_modelcall = paste(
    'inla(
      formula = z ~ 1
        + f(space, model="bym2", graph=slot(sppoly, "nb"), scale.model=TRUE, constr=TRUE),
      family = "normal",
      data= dat,
      control.compute=list(dic=TRUE, waic=TRUE, cpo=FALSE, config=FALSE),  
      control.results=list(return.marginals.random=TRUE, return.marginals.predictor=TRUE ),
      control.predictor=list(compute=FALSE, link=1 ),
      verbose=FALSE
    ) '
  ),   # NOTE:: this is a local model call
  # stmv_au_distance_reference="completely_inside_boundary",
  # stmv_au_distance_reference = "none", 
  # stmv_au_buffer_links = 1, # number of additional neighbours to extend beyond initial solution
  stmv_distance_interpolation=c(5),
  stmv_filter_depth_m = FALSE,  # need data above sea level to get coastline
  stmv_Y_transform =list(
    transf = function(x) {log10(x + 2500)} ,
    invers = function(x) {10^(x) - 2500}
  ), # data range is from -1667 to 5467 m: make all positive valued
  stmv_rsquared_threshold = 0.01, # lower threshold  .. i.e., ignore ... there is no timeseries model, nor a fixed effect spatial "model"
  stmv_distance_statsgrid = 5, # resolution (km) of data aggregation (i.e. generation of the ** statistics ** )
  stmv_nmin = 10,  # min number of data points req before attempting to model in a localized space
  stmv_nmax = 1000, # no real upper bound.. just speed /RAM
  stmv_force_complete_method = "linear_interp",
  stmv_runmode = list(
    carstm = rep("localhost", 1), # serial mode
    globalmodel = FALSE,
    restart_load = FALSE,
    save_completed_data = TRUE # just a dummy variable with the correct name
  )
)


  ncores = ram_local( "ncores", ram_main=?, ram_process=? ) # in GB; 

  p$stmv_runmode$carstm = rep("localhost", ncores)

# p$stmv_distance_prediction_limits = p$stmv_distance_statsgrid * c(1, 1.5 ) # range of permissible predictions km (i.e 1/2 stats grid to upper limit based upon data density)

# NOTE:
(p$stmv_distance_interpolation )
(p$pres)  # grid size

if (0) {
  # to force parallel mode
   ncores = ram_local( "ncores", ram_main=1, ram_process=1 ) # about 24 hr
   p$stmv_runmode = list(
    carstm = rep("localhost", ncores),
    globalmodel = FALSE,
    restart_load = FALSE,
    save_completed_data = TRUE # just a dummy variable with the correct name
   )
}


# quick look of data
  dev.new(); surface( as.image( Z=DATA$input$z, x=DATA$input[, c("plon", "plat")], nx=p$nplons, ny=p$nplats, na.rm=TRUE) )



stmv( p=p  )  # This will take from a few minutes, depending upon system
# stmv_db( p=p, DS="cleanup.all" )


predictions = stmv_db( p=p, DS="stmv.prediction", ret="mean" )
statistics  = stmv_db( p=p, DS="stmv.stats" )
locations =  spatial_grid( p )

# comparison
dev.new(); surface( as.image( Z=predictions, x=locations, nx=p$nplons, ny=p$nplats, na.rm=TRUE) )


statsvars = dimnames(statistics)[[2]]

# statsvars = c("sdTotal", "ndata", "fixed_mean", "fixed_sd", "dic", "dic_p_eff",
#   "waic", "waic_p_eff", "mlik", "Expected_number_of_parameters",
#   "Stdev_of_the_number_of_parameters", "Number_of_equivalent_replicates",
#   "Precision_for_the_Gaussian_observations", "Precision_for_aui",
#   "Phi_for_aui", "Precision_for_the_Gaussian_observations_sd", "Precision_for_aui_sd", "Phi_for_aui_sd"
# )

# statsvars = c( "sdTotal", "rsquared", "ndata", "sdSpatial", "sdObs", "phi", "nu", "localrange" )
dev.new(); levelplot( predictions[] ~ locations[,1] + locations[,2], aspect="iso" )
dev.new(); levelplot( statistics[,match("Phi_for_space", statsvars)]  ~ locations[,1] + locations[,2], aspect="iso" ) # nu
dev.new(); levelplot( statistics[,match("sdTotal", statsvars)]  ~ locations[,1] + locations[,2], aspect="iso" ) #sd total
dev.new(); levelplot( statistics[,match("rsquared", statsvars)]  ~ locations[,1] + locations[,2], aspect="iso" ) #localrange