level1_to_level2_rds.R

######################################################################
##### 52North WPS annotations ##########
######################################################################
# wps.des: id = create_own_tuna_atlas_catch_effort, title = Create your own georeferenced Tuna Atlas dataset of catch or efforts, abstract = This algorithm allows to create own regional or global tuna atlas of geo-referenced gridded catch or efforts. It takes as input the public domain datasets of the five Tuna Regional Fisheries Management Organizations (tRFMOs) (IOTC ICCAT WCPFC IATTC CCSBT) stored within the Tuna atlas database. It proposes a set of parameters to customize the computation of the tuna atlas. ;
# wps.in: id = fact, type = string, title = Variable output of the tuna atlas (catch or effort), value = "catch|effort";
# wps.in: id = include_IOTC, type = string, title = Include IOTC data (Indian Ocean) in the atlas (TRUE or FALSE), value = "TRUE|FALSE";
# wps.in: id = include_ICCAT, type = string, title = Include ICCAT data (Atlantic Ocean) in the tuna atlas?, value = "TRUE|FALSE";
# wps.in: id = include_IATTC, type = string, title = Include IATTC data (Eastern Pacific Ocean) in the tuna atlas?, value = "TRUE|FALSE";
# wps.in: id = include_WCPFC, type = string, title = Include WCPFC data (Western Pacific Ocean) in the tuna atlas?, value = "TRUE|FALSE";
# wps.in: id = include_CCSBT, type = string, title = Include CCSBT data (Southern hemisphere Oceans - only Southern Bluefin Tuna) in the atlas?, value = "TRUE|FALSE";
# wps.in: id = datasets_year_release, type = string, title = Year of release of the datasets by the tRFMOs. First available year is 2017. Usually datasets released in the year Y contain the time series from the beginning of the fisheries (e.g. 1950) to year Y-2 (included). For instance 2017 will extract the datasets released in 2017 and that cover the time series from 1950 to 2015 (included), value = "2017";
# wps.in: id = iccat_ps_include_type_of_school, type = string, title = Concerns ICCAT Purse Seine data. Use only if parameter include_ICCAT is set to TRUE. ICCAT disseminates two catch-and-efforts datasets: one that provides the detail of the type of school (Fad|Free school) for purse seine fisheries only and that starts in 1994 (called Task II catch|effort by operation mode Fad|Free school) and one that does not provide the information of the type of school and that covers all the time period (from 1950) (called Task II catch|effort). These data are redundant (i.e. the data from the dataset Task II catch|effort by operation mode are also available in the dataset Task II catch|effort but in the latter the information on the type of school is not available). Combine both datasets to produce a dataset with fishing mode information (Fad | Free school)? TRUE : both datasets will be combined to produce a dataset with fishing mode information (Fad | free school). FALSE : Only the dataset without the type of school will be used. In that case the output dataset will not have the information on the fishing mode for ICCAT., value = "TRUE|FALSE";
# wps.in: id = iattc_ps_raise_flags_to_schooltype, type = string, title = Concerns IATTC Purse Seine data. Use only if parameter include_IATTC is set to TRUE. For confidentiality policies information on fishing country (flag) and type of school for the geo-referenced catches is available in separate files for the Eastern Pacific Ocean purse seine datasets. IATTC hence provides two public domain dataset: one with the information on the type of school and one with the information on the flag. Both datasets can be combined - using raising methods - to estimate the catches by both flag and type of school for purse seine fisheries. Combine both datasets? TRUE : both datasets (by fishing mode and by flag) will be combined - using raising methods i.e. dataset with the information on the flag will be raised to the dataset with the information on the type of school - to have the detail on both fishing mode and flag for each stratum. FALSE : Only one dataset will be used (either Flag or Type of school). The parameter dimension_to_use_if_no_raising_flags_to_schooltype allows to decide which dataset to use., value = "TRUE|FALSE";
# wps.in: id = iattc_ps_dimension_to_use_if_no_raising_flags_to_schooltype, type = string, title = Concerns IATTC Purse Seine data. Use only if parameter include_IATTC is set to TRUE and parameter iattc_raise_flags_to_schooltype is set to FALSE. In the case IATTC purse seine datasets are not combined (see description of parameter iattc_raise_flags_to_schooltype) which dataset to use? flag : use dataset with the information on flag. Information on type of school will therefore not be available. schooltype : use dataset with the information on type of school. Information on flag will therefore not be available., value = "flag|schooltype";
# wps.in: id = iattc_ps_catch_billfish_shark_raise_to_effort, type = boolean, title = Concerns IATTC Purse Seine data. Use only if parameter include_IATTC is set to TRUE and parameter variable is set to 'catch'. In addition to the separation flag / schooltype (see above) IATTC Purse seine catch-and-effort are available in 3 separate files according to the group of species: tuna, billfishes, sharks. This is due to the fact that PS data is collected from 2 sources: observer and fishing vessel logbooks. Observer records are used when available, and for unobserved trips logbooks are used. Both sources collect tuna data but only observers collect shark and billfish data. So as an example a strata may have observer effort and the number of sets from the observed trips would be counted for tuna and shark and billfish. But there may have also been logbook data for unobserved sets in the same strata so the tuna catch and number of sets for a cell would be added. This would make a higher total number of sets for tuna catch than shark or billfish. So efforts in the billfish and shark datasets might represent only a proportion of the total effort allocated in some strata since it is the observed effort i.e. for which there was an observer onboard. As a result catch in the billfish and shark datasets might represent only a proportion of the total catch allocated in a some strata. TRUE: Raise billfish (resp. shark) catch to the ratio  effort tuna / effort billfish (resp. shark). FALSE: Keep billfish (resp. shark) catch as they are provided in the billfish (resp. shark) catch datasets., value = "TRUE|FALSE";
# wps.in: id = mapping_map_code_lists, type = string, title = Map code lists (gears|species|flags|schooltypes|catchtype)? When using multiple sources of data (i.e. multiple RFMOS) code lists used by the various tRFMOs might be different. They should therefore be mapped to single code lists in order to be able to compare the data. TRUE : map code lists. The url to the datasets providing the code list mappings to use must be set in the parameter mapping_source_mappings. See parameter mapping_source_mappings for more details. FALSE : do not map code lists. Output data will use input codes., value = "TRUE|FALSE" ;
# wps.in: id = mapping_csv_mapping_datasets_url, type = string, title = Use only if parameter mapping_map_code_lists is set to TRUE. Path to the CSV file containing the dimensions that must be mapped and the name of the mapping dataset for each dimension mapped. The mapping datasets must be available in Tuna atlas database.  A template can be found here: https://goo.gl/YZmeDV . , value="http://data.d4science.org/ZWFMa3JJUHBXWk9NTXVPdFZhbU5BUFEyQnhUeWd1d3lHbWJQNStIS0N6Yz0" ;
# wps.in: id = mapping_keep_src_code, type = string, title = Use only if parameter mapping_map_code_lists is set to TRUE. In case of code list mapping (mapping_map_code_lists is TRUE) keep source coding system column? TRUE : conserve in the output dataset both source and target coding systems columns. FALSE : conserve only target coding system. , value="FALSE|TRUE" ;
# wps.in: id = gear_filter, type = string, title = Filter data by gear. Gear codes in this parameter must by the same as the ones used in the catch dataset (i.e. raw tRFMOs gear codes if no mapping or in case of mapping (mapping_map_code_lists is TRUE) codes used in the mapping code list). NULL : do not filter. If you want to filter you must write the codes to filter by separated by a comma in case of multiple codes.  , value = "NULL";
# wps.in: id = unit_conversion_convert, type = string, title = Convert units of measure? if TRUE you must fill in the parameter unit_conversion_df_conversion_factor. , value = "FALSE|TRUE";
# wps.in: id = unit_conversion_csv_conversion_factor_url, type = string, title = Use only if parameter unit_conversion_convert is set to TRUE. If units are converted path to the csv containing the conversion factors dataset. The conversion factor dataset must be properly structured. A template can be found here: https://goo.gl/i7QJYC . The coding systems used in the dimensions of the conversion factors must be the same as the ones used in the catch dataset (i.e. raw tRFMOs codes or in case of mapping (mapping_map_code_lists isTRUE) codes used in the mapping code lists) except for spatial code list. Additional information on the structure are provided here: https://ptaconet.github.io/rtunaatlas//reference/convert_units.html , value = "https://drive.google.com/open?id=1csQ5Ww8QRTaYd1DG8chwuw0UVUOGkjNL";
# wps.in: id = unit_conversion_codelist_geoidentifiers_conversion_factors, type = string, title = Use only if parameter unit_conversion_convert is set to TRUE. If units are converted name of the coding system of the spatial dimension used in the conversion factor dataset (i.e. identifier of the layer in the Tuna atlas database)., value = "areas_conversion_factors_numtoweigth_ird";
# wps.in: id = raising_georef_to_nominal, type = string, title =  Geo-referenced catch data and associated effort can represent only part of the total catches. Raise georeferenced catches to nominal catches? Depending on the availability of the flag dimension (currently not available for the geo-referenced catch-and-effort dataset from the WCPFC and CCSBT) the dimensions used for the raising are either {Flag|Species|Year|Gear} or {Species|Year|Gear}. Some catches cannot be raised because the combination {Flag|Species|Year|Gear} (resp. {Species|Year|Gear}) does exist in the geo-referenced catches but the same combination does not exist in the total catches. In this case non-raised catch data are kept. TRUE : Raise georeferenced catches to total catches. FALSE : Do not raise., value = "TRUE|FALSE";
# wps.in: id = raising_do_not_raise_wcfpc_data, type = string, title =  WCPFC georeferenced data are already raised [terminer la description]. TRUE : Do not Raise WCPFC georeferenced catches to total catches. FALSE : Raise., value = "TRUE|FALSE";
# wps.in: id = raising_raise_only_for_PS_LL, type = string, title =  Raise the data only for Purse Seiners and Longliners (i.e. do not raise for artisanal fisheries) [terminer la description]. TRUE : Raise only for PS and LL. FALSE : Raise for all gears., value = "TRUE|FALSE";
# wps.in: id = aggregate_on_5deg_data_with_resolution_inferior_to_5deg, type = string, title =  Aggregate data that are defined on quadrants or areas inferior to 5° quadrant resolution to corresponding 5° quadrant? TRUE : Aggregate. Data that are provided at spatial resolutions superior to 5° x 5°  will be aggregated to the corresponding 5° quadrant. FALSE : Do not aggregate. Data that are provided at spatial resolutions superior to 5° x 5° will be will be kept as so. , value = "TRUE|FALSE";
# wps.in: id = disaggregate_on_5deg_data_with_resolution_superior_to_5deg, type = string, title = What to do with data that are defined on quadrants or areas superior to 5° quadrant resolution to 5° quadrant? none: Do not do anything. Data that are provided at spatial resolutions inferior to 5° x 5° will be kept as so. disaggregate : data that are provided at spatial resolutions superior to 5° x 5°  will be disaggregated to the corresponding 5°  x 5°  quadrants by dividing the catch equally on the overlappings 5° x 5°  quadrants. remove : Data that are provided at spatial resolutions superior to 5° x 5°  will be removed from the dataset. , value = "none|disaggregate|remove";
# wps.in: id = disaggregate_on_1deg_data_with_resolution_superior_to_1deg, type = string, title = Same as parameter disaggregate_on_5deg_data_with_resolution_superior_to_5deg but for 1° resolutions  , value = "none|disaggregate|remove";
# wps.in: id = spatial_curation_data_mislocated, type = string, title = Some data might be mislocated: either located on land areas or without any area information. This parameter allows to control what to do with these data. reallocate : Reallocate the mislocated data (equally distributed on areas with same dimensions (month|gear|flag|species|schooltype). no_reallocation : do not reallocate mislocated data. The output dataset will keep these data with their original location (eg on land or with no area information). remove : remove the mislocated data., value = "remove|reallocate|no_reallocation";
# wps.in: id = overlapping_zone_iattc_wcpfc_data_to_keep, type = string, title = Concerns IATTC and WCPFC data. IATTC and WCPFC have an overlapping area in their respective area of competence. Which data should be kept for this zone? IATTC : keep data from IATTC. WCPFC : keep data from WCPFC. NULL : Keep data from both tRFMOs. Caution: with the option NULL data in the overlapping zone are likely to be redundant., value = "IATTC|WCPFC|NULL";
# wps.in: id = SBF_data_rfmo_to_keep, type = string, title = Concerns Southern Bluefin Tuna (SBF) data. Use only if parameter fact is set to 'catch' and parameter include_CCSBT is set to TRUE. SBF tuna data do exist in both CCSBT data and the other tuna RFMOs data. Wich data should be kept? CCSBT : CCSBT data are kept for SBF. other_trfmos : data from the other TRFMOs are kept for SBF. NULL : Keep data from all the tRFMOs. Caution: with the option NULL data in the overlapping zones are likely to be redundant., value = "CCSBT|other_trfmos|NULL";
# wps.out: id = zip_namefile, type = text/zip, title = Outputs are 3 csv files: the dataset of georeferenced catches + a dataset of metadata (including informations on the computation, i.e. how the primary datasets were transformed by each correction) [TO DO] + a dataset providing the code lists used for each dimension (column) of the output dataset [TO DO]. All outputs and codes are compressed within a single zip file. ; 

#packages
if(!require(rtunaatlas)){
  if(!require(devtools)){
    install.packages("devtools")
  }
  require(devtools)
  install_github("eblondel/rtunaatlas")
  require(rtunaatlas)
}

if(!require(dplyr)){
  install.packages("dplyr")
  require(dplyr)
}

if(!require(data.table)){
  install.packages("data.table")
  require(data.table)
}

#scripts
url_scripts_create_own_tuna_atlas <- "https://raw.githubusercontent.com/eblondel/geoflow-tunaatlas/master/tunaatlas_scripts/generation"
source(file.path(url_scripts_create_own_tuna_atlas, "get_rfmos_datasets_level0.R")) #modified for geoflow
source(file.path(url_scripts_create_own_tuna_atlas, "retrieve_nominal_catch.R")) #modified for geoflow
source(file.path(url_scripts_create_own_tuna_atlas, "map_codelists.R")) #modified for geoflow
source(file.path(url_scripts_create_own_tuna_atlas, "convert_units.R")) #modified for geoflow
source("https://raw.githubusercontent.com/BastienIRD/Tunaatlas_level1/main/fonction_dossier.R")

# connect to Tuna atlas database
con <- config$software$output$dbi

#set parameterization
fact <- options$fact
raising_georef_to_nominal <- options$raising_georef_to_nominal
iattc_ps_raise_flags_to_schooltype <- options$iattc_ps_raise_flags_to_schooltype
iattc_ps_dimension_to_use_if_no_raising_flags_to_schooltype <- options$iattc_ps_dimension_to_use_if_no_raising_flags_to_schooltype
iattc_ps_catch_billfish_shark_raise_to_effort <- options$iattc_ps_catch_billfish_shark_raise_to_effort
iccat_ps_include_type_of_school <- options$iccat_ps_include_type_of_school

#Identify expected Level of processing
DATA_LEVEL <- unlist(strsplit(entity$identifiers[["id"]], "_level"))[2]


#-----------------------------------------------------------------------------------------------------------------------------------------------------------
#LEVEL 0 FIRMS PRODUCTS
#-----------------------------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------------------
config$logger.info("LEVEL 1 => STEP 1/5: Extract and load IRD Level 0 gridded catch data input")
#-----------------------------------------------------------------------------------------------------------------------------------------------------------
dataset <- readr::read_csv(entity$getJobDataResource(config, entity$data$source[[1]]), guess_max = 0)


dataset$time_start<-substr(as.character(dataset$time_start), 1, 10)
dataset$time_end<-substr(as.character(dataset$time_end), 1, 10)
georef_dataset<-dataset
class(georef_dataset$value) <- "numeric"
fonction_dossier("endlevel0data",
                 georef_dataset)
#@juldebar patch to fix errors due to the last step of Level 0 workflow
if(any(georef_dataset$unit == "t")) georef_dataset[georef_dataset$unit == "t", ]$unit <- "MT"
if(any(georef_dataset$unit == "no")) georef_dataset[georef_dataset$unit == "no", ]$unit <- "NO"
config$logger.info(sprintf("Gridded catch dataset has [%s] lines", nrow(georef_dataset)))
config$logger.info(sprintf("Gridded catch dataset for 'MT' unit only has [%s] lines", nrow(georef_dataset %>% filter(unit=="MT"))))
config$logger.info(sprintf("Gridded catch dataset for 'NO' unit only has [%s] lines", nrow(georef_dataset %>% filter(unit=="NO"))))
rm(dataset)

config$logger.info("LEVEL 0 => STEP 3/8: Apply filters on fishing gears if needed (Filter data by groups of gears) ")
#-----------------------------------------------------------------------------------------------------------------------------------------------------------
if (!is.null(options$gear_filter)){
  gear_filter<-unlist(strsplit(options$gear_filter, split=","))
  config$logger.info(sprintf("Filtering by gear(s) [%s]", paste(gear_filter, collapse=",")))	
  georef_dataset<-georef_dataset %>% dplyr::filter(gear %in% gear_filter)
  config$logger.info("Filtering gears OK")
  config$logger.info(sprintf("Gridded catch dataset has [%s] lines", nrow(georef_dataset)))	
  fonction_dossier("gearfilter",
                   georef_dataset)
}

#-----------------------------------------------------------------------------------------------------------------------------------------------------------
config$logger.info("LEVEL 0 => STEP 6/8: Spatial Aggregation of data (5deg resolution datasets only: Aggregate data on 5° resolution quadrants)")
#-----------------------------------------------------------------------------------------------------------------------------------------------------------
if(!is.null(options$aggregate_on_5deg_data_with_resolution_inferior_to_5deg)) if (options$aggregate_on_5deg_data_with_resolution_inferior_to_5deg) {
  
  config$logger.info("Aggregating data that are defined on quadrants or areas inferior to 5° quadrant resolution to corresponding 5° quadrant...")
  georef_dataset<-rtunaatlas::spatial_curation_upgrade_resolution(con, georef_dataset, 5)
  georef_dataset<-georef_dataset$df
  
  # fill metadata elements
  # lineage<-"Data that were provided at spatial resolutions inferior to 5° x 5°  were aggregated to the corresponding 5° x 5°  quadrant."
  # aggregate_step = geoflow_process$new()
  # aggregate_step$setRationale(lineage)
  # aggregate_step$setProcessor(firms_contact)  #TODO define who's the processor
  # entity$provenance$processes <- c(entity$provenance$processes, aggregate_step)	
  # entity$descriptions[["abstract"]] <- paste0(entity$descriptions[["abstract"]], "\n", "- Data that were provided at resolutions inferior to 5° x 5°  were aggregated to the corresponding 5° x 5°  quadrant.")
  
  
  config$logger.info("Aggregating data that are defined on quadrants or areas inferior to 5° quadrant resolution to corresponding 5° quadrant OK")
  config$logger.info(sprintf("Gridded catch dataset has [%s] lines", nrow(georef_dataset)))	
  fonction_dossier("aggregation",
                   georef_dataset)
}

#-----------------------------------------------------------------------------------------------------------------------------------------------------------
#LEVEL 1 IRD PRODUCTS
#-----------------------------------------------------------------------------------------------------------------------------------------------------------
config$logger.info(paste0("Total ",fact," after raising is now: ",sum(georef_dataset$value),"\n"))
config$logger.info(sprintf("Gridded catch dataset has [%s] lines", nrow(georef_dataset)))	
config$logger.info(paste0("Total catch for data after raising is ",sum(georef_dataset$value),"  \n"))

config$logger.info("Start generation of Level 1 products")

if(!is.null(options$raising_georef_to_nominal)) if (options$raising_georef_to_nominal){
  config$logger.info("Retrieving RFMOs nominal catch...")
  nominal_catch <- readr::read_csv(entity$getJobDataResource(config, entity$data$source[[2]]), guess_max = 0)
  #@juldebar keep same units for all datatets
  if(any(nominal_catch$unit == "t")) nominal_catch[nominal_catch$unit == "t", ]$unit <- "MT"
  if(any(nominal_catch$unit == "no")) nominal_catch[nominal_catch$unit == "no", ]$unit <- "NO"
  class(nominal_catch$value) <- "numeric"
  config$logger.info("Retrieving RFMOs nominal catch OK")
  config$logger.info(sprintf("nominal catch dataset has [%s] lines", nrow(nominal_catch)))	
  config$logger.info(sprintf("Gridded catch dataset has [%s] lines", nrow(georef_dataset)))	
  
}

if(!is.null(options$unit_conversion_convert)) if (options$unit_conversion_convert){
  config$logger.info("-----------------------------------------------------------------------------------------------------")
  config$logger.info(sprintf("LEVEL 1 => STEP 2/5  for file [%s] is executed: Convert units by using A. Fonteneau file. Option is: [%s] ",entity$data$source[[2]], options$unit_conversion_convert))
  config$logger.info("-----------------------------------------------------------------------------------------------------")
  
  mapping_map_code_lists <- TRUE
  if(!is.null(options$mapping_map_code_lists)) mapping_map_code_lists = options$mapping_map_code_lists
  if(is.null(options$unit_conversion_csv_conversion_factor_url)) stop("Conversion of unit requires parameter 'unit_conversion_csv_conversion_factor_url'")
  if(is.null(options$unit_conversion_codelist_geoidentifiers_conversion_factors)) stop("Conversion of unit requires parameter 'unit_conversion_codelist_geoidentifiers_conversion_factors'")
  
  ntons_before_this_step <- round(georef_dataset %>% filter(unit=="MT")  %>% select(value)  %>% sum())
  config$logger.info(sprintf("STEP 2/5 : Gridded catch dataset before unit conversion has [%s] lines and total catch is [%s] Tons", nrow(georef_dataset),ntons_before_this_step))	
  
  config$logger.info("STEP 2/5: BEGIN do_unit_conversion() function to convert units of georef_dataset") 
  georef_dataset <- do_unit_conversion(entity=entity,
                                       config=config,
                                       fact=fact,
                                       unit_conversion_csv_conversion_factor_url=options$unit_conversion_csv_conversion_factor_url,
                                       unit_conversion_codelist_geoidentifiers_conversion_factors=options$unit_conversion_codelist_geoidentifiers_conversion_factors,
                                       mapping_map_code_lists=mapping_map_code_lists,
                                       georef_dataset=georef_dataset)
  config$logger.info("STEP 2/5: END do_unit_conversion() function")
  
  ntons_after_conversion <- round(georef_dataset %>% select(value)  %>% sum())
  config$logger.info(sprintf("STEP 2/5 : Gridded catch dataset after unit conversion has [%s] lines and total catch is [%s] Tons", nrow(georef_dataset),ntons_after_conversion))	
  # config$logger.info(sprintf("STEP 2/5 : [%s] lines have been removed", nrow(georef_dataset)-nrow_before))
  config$logger.info(sprintf("STEP 2/5 : Unit conversion generated [%s] additionnal tons", ntons_after_conversion-ntons_before_this_step))
  config$logger.info(sprintf("STEP 2/5 : Total number for 'NO' unit is now [%s] individuals", georef_dataset %>% filter(unit=="NO")  %>% select(value)  %>% sum()))
  config$logger.info("END STEP 2/5")
  fonction_dossier("unitconversion",
                   georef_dataset)
}else{
  config$logger.info("-----------------------------------------------------------------------------------------------------")
  config$logger.info(sprintf("LEVEL 1 => STEP 2/5 not executed  for file [%s] (since not selected in the workflow options, see column 'Data' of geoflow entities spreadsheet): Convert units by using A. Fonteneau file. Option is: [%s] ",entity$data$source[[2]], options$unit_conversion_convert))
  config$logger.info("-----------------------------------------------------------------------------------------------------")
}


if (options$spatial_curation_data_mislocated %in% c("reallocate","remove")){
  
  config$logger.info("-----------------------------------------------------------------------------------------------------")
  config$logger.info(sprintf("LEVEL 1 => STEP 3/5  for file [%s] is executed: Reallocation of mislocated data  (i.e. on land areas or without any spatial information) (data with no spatial information have the dimension 'geographic_identifier' set to 'UNK/IND' or 'NA'). Option is: [%s] ",entity$data$source[[1]], options$spatial_curation_data_mislocated))
  config$logger.info("-----------------------------------------------------------------------------------------------------")
  
  ntons_before_this_step <- round(georef_dataset %>% select(value)  %>% sum())
  config$logger.info(sprintf("STEP 3/5 : Gridded catch dataset before Reallocation of mislocated data has [%s] lines and total catch is [%s] Tons", nrow(georef_dataset),ntons_before_this_step))	
  
  source(file.path(url_scripts_create_own_tuna_atlas, "spatial_curation_data_mislocated.R")) #modified for geoflow
  config$logger.info("STEP 3/5: BEGIN function_spatial_curation_data_mislocated() function")
  georef_dataset<-function_spatial_curation_data_mislocated(entity=entity,
                                                            config=config,
                                                            df=georef_dataset,
                                                            spatial_curation_data_mislocated=options$spatial_curation_data_mislocated
  )
  config$logger.info("STEP 3/5: END function_spatial_curation_data_mislocated() function")
  
  #@juldebar: pending => metadata elements below to be managed (commented for now)
  # metadata$description<-paste0(metadata$description,georef_dataset$description)
  # metadata$lineage<-c(metadata$lineage,georef_dataset$lineage)
  
  georef_dataset<-georef_dataset$dataset
  ntons_after_mislocated <- round(georef_dataset %>% select(value)  %>% sum())
  config$logger.info(sprintf("STEP 3/5 : Gridded catch dataset after Reallocation of mislocated data has [%s] lines and total catch is [%s] Tons", nrow(georef_dataset),ntons_after_mislocated))	
  config$logger.info(sprintf("STEP 3/5 : Reallocation of mislocated data generated [%s] additionnal tons", ntons_after_mislocated-ntons_before_this_step))
  config$logger.info("END STEP 3/5")
  fonction_dossier("realocation",
                   georef_dataset)
}else{
  config$logger.info("-----------------------------------------------------------------------------------------------------")
  config$logger.info(sprintf("LEVEL 1 => STEP 3/5 not executed  for file [%s] (since not selected in the workflow options, see column 'Data' of geoflow entities spreadsheet):  Reallocation of mislocated data  (i.e. on land areas or without any spatial information) (data with no spatial information have the dimension 'geographic_identifier' set to 'UNK/IND' or 'NA'). Option is: [%s] ",entity$data$source[[1]], options$spatial_curation_data_mislocated))
  config$logger.info("-----------------------------------------------------------------------------------------------------")
}

if (options$disaggregate_on_5deg_data_with_resolution_superior_to_5deg %in% c("disaggregate","remove")) {
  
  config$logger.info("-----------------------------------------------------------------------------------------------------")
  config$logger.info(sprintf("LEVEL 1 => STEP 4/5  for file [%s] is executed: Disaggregate data on 5° resolution quadrants (for 5deg resolution datasets only). Option is: [%s] ",entity$data$source[[1]], options$disaggregate_on_5deg_data_with_resolution_superior_to_5deg))
  config$logger.info("-----------------------------------------------------------------------------------------------------")
  
  source(file.path(url_scripts_create_own_tuna_atlas, "disaggregate_on_resdeg_data_with_resolution_superior_to_resdeg.R"))
  
  ntons_before_this_step <- round(georef_dataset %>% select(value)  %>% sum())
  config$logger.info(sprintf("STEP 4/5 : Gridded catch dataset before Disaggregate data on 5° resolution has [%s] lines and total catch is [%s] Tons", nrow(georef_dataset),ntons_before_this_step))	
  
  config$logger.info("STEP 4/5: BEGIN function_disaggregate_on_resdeg_data_with_resolution_superior_to_resdeg() function")
  georef_dataset<-function_disaggregate_on_resdeg_data_with_resolution_superior_to_resdeg(entity,config,options,
                                                                                          georef_dataset=georef_dataset,
                                                                                          resolution=5,
                                                                                          action_to_do=options$disaggregate_on_5deg_data_with_resolution_superior_to_5deg)
  config$logger.info("STEP 4/5: END function_disaggregate_on_resdeg_data_with_resolution_superior_to_resdeg() function")
  
  
  #@juldebar: pending => metadata elements below to be managed (commented for now)
  # metadata$description<-paste0(metadata$description,georef_dataset$description)
  # metadata$lineage<-c(metadata$lineage,georef_dataset$lineage)
  
  georef_dataset<-georef_dataset$dataset
  ntons_after_disaggregation_5deg <- round(georef_dataset %>% select(value)  %>% sum())
  config$logger.info(sprintf("STEP 4/5 : Gridded catch dataset after Disaggregate data on 5° resolution has [%s] lines and total catch is [%s] Tons", nrow(georef_dataset),ntons_after_disaggregation_5deg))	
  config$logger.info(sprintf("STEP 4/5 : Disaggregate data on 5° generated [%s] additionnal tons", ntons_after_disaggregation_5deg-ntons_before_this_step))
  config$logger.info("END STEP 4/5")
  fonction_dossier("disagreggation5deg",
                   georef_dataset)
}else{
  config$logger.info("-----------------------------------------------------------------------------------------------------")
  config$logger.info(sprintf("LEVEL 1 => STEP 4/5 not executed  for file [%s] (since not selected in the workflow options, see column 'Data' of geoflow entities spreadsheet):  Disaggregate data on 5° resolution quadrants (for 5deg resolution datasets only). Option is: [%s] ",entity$data$source[[1]], options$disaggregate_on_5deg_data_with_resolution_superior_to_5deg))
  config$logger.info("-----------------------------------------------------------------------------------------------------")
}

if (options$disaggregate_on_1deg_data_with_resolution_superior_to_1deg %in% c("disaggregate","remove")) { 
  
  config$logger.info("-----------------------------------------------------------------------------------------------------")
  config$logger.info(sprintf("LEVEL 1 => STEP 5/5 for file [%s] is executed: Disaggregate data on 1° resolution quadrants (for 1deg resolution datasets only). Option is: [%s] ",entity$data$source[[1]], options$disaggregate_on_1deg_data_with_resolution_superior_to_1deg))
  config$logger.info("-----------------------------------------------------------------------------------------------------")
  
  ntons_before_this_step <- round(georef_dataset %>% select(value)  %>% sum())
  config$logger.info(sprintf("STEP 5/5 : Gridded catch dataset before Disaggregate data on 1° has [%s] lines and total catch is [%s] Tons", nrow(georef_dataset),ntons_before_this_step))	
  
  source(file.path(url_scripts_create_own_tuna_atlas, "disaggregate_on_resdeg_data_with_resolution_superior_to_resdeg.R"))
  config$logger.info("STEP 5/5: BEGIN function_disaggregate_on_resdeg_data_with_resolution_superior_to_resdeg() function")
  georef_dataset<-function_disaggregate_on_resdeg_data_with_resolution_superior_to_resdeg(entity,config,options,
                                                                                          georef_dataset=georef_dataset,
                                                                                          resolution=1,
                                                                                          action_to_do=options$disaggregate_on_1deg_data_with_resolution_superior_to_1deg)
  config$logger.info("STEP 5/5: END function_disaggregate_on_resdeg_data_with_resolution_superior_to_resdeg() function")
  
  #@juldebar: pending => metadata elements below to be managed (commented for now)
  # metadata$description<-paste0(metadata$description,georef_dataset$description)
  # metadata$lineage<-c(metadata$lineage,georef_dataset$lineage)
  
  georef_dataset<-georef_dataset$dataset
  ntons_after_disaggregation_1deg <- round(georef_dataset %>% select(value)  %>% sum())
  config$logger.info(sprintf("STEP 5/5 : Gridded catch dataset after Disaggregate data on 1° has [%s] lines and total catch is now [%s] Tons", nrow(georef_dataset),ntons_after_disaggregation_1deg))	
  config$logger.info(sprintf("STEP 5/5 : Disaggregate data on 1° generated [%s] additionnal tons", ntons_after_disaggregation_1deg-ntons_before_this_step))
  config$logger.info("END STEP 5/5")
  fonction_dossier("disaggregation1deg",
                   georef_dataset)
} else{
  config$logger.info("-----------------------------------------------------------------------------------------------------")
  config$logger.info(sprintf("LEVEL 1 => STEP 5/5 not executed  for file [%s] (since not selected in the workflow options, see column 'Data' of geoflow entities spreadsheet): Disaggregate data on 1° resolution quadrants (for 1deg resolution datasets only). Option is: [%s] ",entity$data$source[[1]], options$disaggregate_on_1deg_data_with_resolution_superior_to_1deg))
  config$logger.info("-----------------------------------------------------------------------------------------------------")
}

# 
# #-----------------------------------------------------------------------------------------------------------------------------------------------------------
# #LEVEL 2 IRD PRODUCTS
# #-----------------------------------------------------------------------------------------------------------------------------------------------------------

config$logger.info("-----------------------------------------------------------------------------------------------------")
config$logger.info("LEVEL 2 => STEP 1/3: Set parameters")
config$logger.info("-----------------------------------------------------------------------------------------------------")
raising_georef_to_nominal <- options$raising_georef_to_nominal
iattc_ps_raise_flags_to_schooltype <- options$iattc_ps_raise_flags_to_schooltype
iattc_ps_dimension_to_use_if_no_raising_flags_to_schooltype <- options$iattc_ps_dimension_to_use_if_no_raising_flags_to_schooltype
iattc_ps_catch_billfish_shark_raise_to_effort <- options$iattc_ps_catch_billfish_shark_raise_to_effort
iccat_ps_include_type_of_school <- options$iccat_ps_include_type_of_school

config$logger.info("-----------------------------------------------------------------------------------------------------")
config$logger.info("LEVEL 2 => STEP 2/3: Extract and load IRD Level 1 gridded catch data input")
config$logger.info("-----------------------------------------------------------------------------------------------------")


if(!is.null(options$raising_georef_to_nominal)) if (options$raising_georef_to_nominal){  
  config$logger.info("-----------------------------------------------------------------------------------------------------")
  config$logger.info("LEVEL 2 => STEP 3/3: Raise IRD gridded Level 1 (1 or 5 deg) input with FIRMS Level O total (nominal) catch dataset")
  config$logger.info("-----------------------------------------------------------------------------------------------------")
  source(file.path(url_scripts_create_own_tuna_atlas, "raising_georef_to_nominal.R")) #modified for geoflow
  
  config$logger.info("Extract and load FIRMS Level 0 nominal catch data input (required if raising process is asked) ")
  # 	nominal_catch <- readr::read_csv(entity$getJobDataResource(config, entity$data$source[[1]]), guess_max = 0)
  #         #@juldebar keep same units for all datatets
  # 	if(any(nominal_catch$unit == "t")) nominal_catch[nominal_catch$unit == "t", ]$unit <- "MT"
  #         if(any(nominal_catch$unit == "no")) nominal_catch[nominal_catch$unit == "no", ]$unit <- "NO"
  # 	class(nominal_catch$value) <- "numeric"
  #@juldebar if not provided by Google drive line below should be used if nominal catch has to be extracted from the database
  #nominal_catch <-retrieve_nominal_catch(entity, config, options)
  config$logger.info(sprintf("Nominal catch dataset has [%s] lines", nrow(nominal_catch)))	
  config$logger.info(paste0("Total of  nominal catch for file ",entity$data$source[[2]], "is : ",sum(nominal_catch$value),"  \n"))
  
  config$logger.info("Start raising process")
  
  if (fact=="catch"){
    config$logger.info("Fact=catch !")
    dataset_to_compute_rf=georef_dataset
    #@juldebar why do we use "year' as time dimension here ?
    x_raising_dimensions=c("fishingfleet","gear","species","year","source_authority")
    
  } else if (fact=="effort"){    ## If we raise the efforts, the RF is calculated using the georeferenced catch data. Hence, we need to retrieve the georeferenced catch data.
    cat("Catch datasets must be retrieved and processed in order to raise efforts. \nRetrieving georeferenced catch datasets from the Tuna atlas database...\n")
    dataset_catch<-NULL
    if (include_IOTC=="TRUE"){
      rfmo_dataset<-rtunaatlas::get_rfmos_datasets_level0("IOTC","catch",datasets_year_release)
      dataset_catch<-rbind(dataset_catch,rfmo_dataset)
      rm(rfmo_dataset)
    }
    if (include_WCPFC=="TRUE"){
      rfmo_dataset<-rtunaatlas::get_rfmos_datasets_level0("WCPFC","catch",datasets_year_release)
      dataset_catch<-rbind(dataset_catch,rfmo_dataset)
      rm(rfmo_dataset)
    }
    if (include_CCSBT=="TRUE"){
      rfmo_dataset<-rtunaatlas::get_rfmos_datasets_level0("CCSBT","catch",datasets_year_release)
      dataset_catch<-rbind(dataset_catch,rfmo_dataset)
      rm(rfmo_dataset)
    }
    if (include_IATTC=="TRUE"){
      rfmo_dataset<-rtunaatlas::get_rfmos_datasets_level0("IATTC",
                                                          "catch",
                                                          datasets_year_release,
                                                          iattc_ps_raise_flags_to_schooltype=iattc_ps_raise_flags_to_schooltype,
                                                          iattc_ps_dimension_to_use_if_no_raising_flags_to_schooltype=iattc_ps_dimension_to_use_if_no_raising_flags_to_schooltype,
                                                          iattc_ps_catch_billfish_shark_raise_to_effort=TRUE)
      dataset_catch<-rbind(dataset_catch,rfmo_dataset)
      rm(rfmo_dataset)
    }
    if (include_ICCAT=="TRUE"){
      rfmo_dataset<-rtunaatlas::get_rfmos_datasets_level0("ICCAT",
                                                          "catch",
                                                          datasets_year_release,
                                                          iccat_ps_include_type_of_school=iccat_ps_include_type_of_school)
      dataset_catch<-rbind(dataset_catch,rfmo_dataset)
      rm(rfmo_dataset)
    }
    
    
    if (mapping_map_code_lists=="TRUE"){
      dataset_catch<-function_map_code_lists("catch",mapping_csv_mapping_datasets_url,dataset_catch,mapping_keep_src_code)$dataset
    }
    
    if (!is.null(gear_filter)){
      dataset_catch<-function_gear_filter(gear_filter,dataset_catch)$dataset
    }
    dataset_catch$time_start<-substr(as.character(dataset_catch$time_start), 1, 10)
    dataset_catch$time_end<-substr(as.character(dataset_catch$time_end), 1, 10)
    if (unit_conversion_convert=="TRUE"){ 
      # We use our conversion factors (IRD). This should be an input parameter of the script
      #@juldebar URL for unit_conversion_csv_conversion_factor_url of should not be hard coded, temporary patch
      dataset_catch<-function_unit_conversion_convert(con,
                                                      fact="catch",
                                                      unit_conversion_csv_conversion_factor_url="https://drive.google.com/open?id=1csQ5Ww8QRTaYd1DG8chwuw0UVUOGkjNL",
                                                      unit_conversion_codelist_geoidentifiers_conversion_factors="areas_conversion_factors_numtoweigth_ird",
                                                      mapping_map_code_lists,
                                                      dataset_catch)$dataset
    }
    
    dataset_to_compute_rf=dataset_catch
    #@juldebar insert patch below to fix error in rtunaatlas::raise_get_rf function
    
    rm(dataset_catch)
    #@juldebar : update with the new name of "flag" dimension (now "fishingfleet")
    x_raising_dimensions=c("fishingfleet","gear","year","source_authority")
  }
  
  class(dataset_to_compute_rf$value) <- "numeric"
  
  
  config$logger.info("Executing function function_raising_georef_to_nominal")
  config$logger.info(paste0("Total ",fact," before raising is : ",sum(georef_dataset$value),"\n"))
  config$logger.info(paste0("Total ",fact," in nominal data is : ",sum(nominal_catch$value),"\n"))
  
  georef_dataset<-function_raising_georef_to_nominal(entity=entity,
                                                     config=config,
                                                     dataset_to_raise=georef_dataset,
                                                     nominal_dataset_df=nominal_catch,
                                                     # nominal_catch,
                                                     # dataset_to_compute_rf=nominal_catch,
                                                     dataset_to_compute_rf=dataset_to_compute_rf,
                                                     x_raising_dimensions=x_raising_dimensions)
  
  rm(dataset_to_compute_rf)
  
  #@juldebar: pending => metadata elements below to be managed (commented for now)
  # metadata$description<-paste0(metadata$description,georef_dataset$description)
  # metadata$lineage<-c(metadata$lineage,georef_dataset$lineage)
  # metadata$supplemental_information<-paste0(metadata$supplemental_information,georef_dataset$supplemental_information)
  
  georef_dataset<-georef_dataset$dataset
  config$logger.info(paste0("Total ",fact," after raising is now: ",sum(georef_dataset$value),"\n"))
  config$logger.info(sprintf("Gridded catch dataset has [%s] lines", nrow(georef_dataset)))	
  config$logger.info(paste0("Total catch for data after raising is ",sum(georef_dataset$value),"  \n"))
  fonction_dossier("level2",
                   georef_dataset)
}else{
  config$logger.info("LEVEL 2 => STEP 3/3 not executed (since not selected in the workflow options (see column 'Data' of geoflow entities spreadsheet)")
} 


config$logger.info("-----------------------------------------------------------------------------------------------------")
config$logger.info("ALL LEVELS (FINAL STEP): restructuring dataset before LOADING (in DRIVE / POSTGIS....)")
config$logger.info("-----------------------------------------------------------------------------------------------------")


dataset<-georef_dataset %>% group_by(.dots = setdiff(colnames(georef_dataset),"value")) %>% dplyr::summarise(value=sum(value))
dataset<-data.frame(dataset)

#----------------------------------------------------------------------------------------------------------------------------
#@eblondel additional formatting for next time support
dataset$time_start <- as.Date(dataset$time_start)
dataset$time_end <- as.Date(dataset$time_end)
#we enrich the entity with temporal coverage
dataset_temporal_extent <- paste(as.character(min(dataset$time_start)), as.character(max(dataset$time_end)), sep = "/")
entity$setTemporalExtent(dataset_temporal_extent)
#if there is any entity relation with name 'codelists' we read the file
df_codelists <- NULL
cl_relations <- entity$relations[sapply(entity$relations, function(x){x$name=="codelists"})]
if(length(cl_relations)>0){
  config$logger.info("Appending codelists to global dataset generation action output")
  googledrive_baseurl <- "https://drive.google.com/open?id="
  if(startsWith(cl_relations[[1]]$link, googledrive_baseurl)){
    #managing download through google drive
    config$logger.info("Downloading file using Google Drive R interface")
    drive_id <- unlist(strsplit(cl_relations[[1]]$link, "id="))[2]
    drive_id <- unlist(strsplit(drive_id, "&export"))[1] #control in case export param is appended
    googledrive::drive_download(file = googledrive::as_id(drive_id), path = file.path("data", paste0(entity$identifiers[["id"]], "_codelists.csv")))
    df_codelists <- read.csv(file.path("data", paste0(entity$identifiers[["id"]], "_codelists.csv")))
  }else{
    df_codelists <- read.csv(cl_relations[[1]]$link)
  }
}


#@geoflow -> output structure as initially used by https://raw.githubusercontent.com/ptaconet/rtunaatlas_scripts/master/workflow_etl/scripts/generate_dataset.R
dataset <- list(
  dataset = dataset, 
  additional_metadata = NULL, #nothing here
  codelists = df_codelists #in case the entity was provided with a link to codelists
)


if (fact=="effort" & DATA_LEVEL %in% c("1", "2")){
  # Levels 1 and 2 of non-global datasets should be expressed with tRFMOs code lists. However, for the effort unit code list and in those cases, we take the tuna atlas effort unit codes although this is not perfect. but going back to tRFMOs codes is too complicated 
  df_codelists$code_list_identifier[which(df_codelists$dimension=="unit")]<-"effortunit_rfmos"
}

#@geoflow -> export as csv
output_name_dataset <- file.path("data", paste0(entity$identifiers[["id"]], "_harmonized.csv"))
# write.csv(dataset$dataset, output_name_dataset, row.names = FALSE)
output_name_codelists <- file.path("data", paste0(entity$identifiers[["id"]], "_codelists.csv"))
# write.csv(dataset$codelists, output_name_codelists, row.names = FALSE)
#----------------------------------------------------------------------------------------------------------------------------  
entity$addResource("harmonized", output_name_dataset)
entity$addResource("codelists", output_name_codelists)
entity$addResource("geom_table", options$geom_table)

#### END
config$logger.info("-----------------------------------------------------------------------------------------------------")
config$logger.info("End: Your tuna atlas dataset has been created!")
config$logger.info("-----------------------------------------------------------------------------------------------------")