Provides easy to use idioms for working with NetCDF data for extraction, manipulation and visualization. NetCDF is Network Common Data Form https://www.unidata.ucar.edu/software/netcdf/.
Details
See print.tidync() for details on the printed version of a tidync object.
There is a family of functions "hyper_verb" around exploring and extracting data.
active | report the currently active grid |
activate | active a grid |
tidync | core NetCDF source object for tidync functions |
hyper_filter | apply dimension expressions to specify array slices |
hyper_array | extracts a raw data array based on a NetCDF index |
hyper_tbl_cube | extracts data as a dplyr tbl_cube |
hyper_tibble | extracts data as a data frame with all dimension values |
hyper_transforms | extract the active (or all) dimension transforms |
hyper_vars | information on active variables |
hyper_dims | information on active dimensions |
hyper_grids | information on grids |
The scheme generally processes dimension filters into NetCDF extraction indexes and these are always available to each function, and are expressed in printed output.
The following options are available.
tidync.large.data.check = TRUE/FALSE | check for large data extraction (default TRUE) |
tidync.silent = FALSE/TRUE | emit warnings,messages or be silent (default FALSE) |
Examples
argofile <- system.file("extdata/argo/MD5903593_001.nc", package = "tidync")
argo <- tidync(argofile)
argo %>% active()
#> [1] "D10,D8"
argo %>% activate("D3,D8") %>% hyper_array()
#> Class: tidync_data (list of tidync data arrays)
#> Variables (2): 'PLATFORM_NUMBER', 'POSITIONING_SYSTEM'
#> Dimension (1): STRING8,N_PROF (2)
#> Source: /usr/local/lib/R/site-library/tidync/extdata/argo/MD5903593_001.nc
argo %>% hyper_filter(N_LEVELS = index < 4)
#>
#> Data Source (1): MD5903593_001.nc ...
#>
#> Grids (16) <dimension family> : <associated variables>
#>
#> [1] D0,D9,D11,D8 : SCIENTIFIC_CALIB_DATE
#> [2] D6,D9,D11,D8 : PARAMETER
#> [3] D7,D9,D11,D8 : SCIENTIFIC_CALIB_EQUATION, SCIENTIFIC_CALIB_COEFFICIENT, SCIENTIFIC_CALIB_COMMENT
#> [4] D6,D9,D8 : STATION_PARAMETERS
#> [5] D10,D8 : PRES, PRES_QC, PRES_ADJUSTED, PRES_ADJUSTED_QC, PRES_ADJUSTED_ERROR, TEMP, TEMP_QC, TEMP_ADJUSTED, TEMP_ADJUSTED_QC, TEMP_ADJUSTED_ERROR, PSAL, PSAL_QC, PSAL_ADJUSTED, PSAL_ADJUSTED_QC, PSAL_ADJUSTED_ERROR, DOXY, DOXY_QC, DOXY_ADJUSTED, DOXY_ADJUSTED_QC, DOXY_ADJUSTED_ERROR, CHLA, CHLA_QC, CHLA_ADJUSTED, CHLA_ADJUSTED_QC, CHLA_ADJUSTED_ERROR, BBP700, BBP700_QC, BBP700_ADJUSTED, BBP700_ADJUSTED_QC, BBP700_ADJUSTED_ERROR, NITRATE, NITRATE_QC, NITRATE_ADJUSTED, NITRATE_ADJUSTED_QC, NITRATE_ADJUSTED_ERROR **ACTIVE GRID** ( 986 values per variable)
#> [6] D1,D8 : DATA_CENTRE
#> [7] D2,D8 : DATA_STATE_INDICATOR, WMO_INST_TYPE
#> [8] D3,D8 : PLATFORM_NUMBER, POSITIONING_SYSTEM
#> [9] D5,D8 : DC_REFERENCE, PLATFORM_TYPE, FLOAT_SERIAL_NO, FIRMWARE_VERSION
#> [10] D6,D8 : PROJECT_NAME, PI_NAME
#> [11] D7,D8 : VERTICAL_SAMPLING_SCHEME
#> [12] D9,D8 : PARAMETER_DATA_MODE
#> [13] D0 : REFERENCE_DATE_TIME, DATE_CREATION, DATE_UPDATE
#> [14] D2 : FORMAT_VERSION, HANDBOOK_VERSION
#> [15] D5 : DATA_TYPE
#> [16] D8 : CYCLE_NUMBER, DIRECTION, DATA_MODE, JULD, JULD_QC, JULD_LOCATION, LATITUDE, LONGITUDE, POSITION_QC, CONFIG_MISSION_NUMBER, PROFILE_PRES_QC, PROFILE_TEMP_QC, PROFILE_PSAL_QC, PROFILE_DOXY_QC, PROFILE_CHLA_QC, PROFILE_BBP700_QC, PROFILE_NITRATE_QC
#>
#> Dimensions 14 (2 active):
#>
#> dim name length min max start count dmin dmax unlim coord_dim
#> <chr> <chr> <dbl> <dbl> <dbl> <int> <int> <dbl> <dbl> <lgl> <lgl>
#> 1 D8 N_PROF 2 1 2 1 2 1 2 FALSE FALSE
#> 2 D10 N_LEVELS 493 1 493 1 3 1 3 FALSE FALSE
#>
#> Inactive dimensions:
#>
#> dim name length min max unlim coord_dim
#> <chr> <chr> <dbl> <dbl> <dbl> <lgl> <lgl>
#> 1 D0 DATE_TIME 14 1 14 FALSE FALSE
#> 2 D1 STRING2 2 1 2 FALSE FALSE
#> 3 D2 STRING4 4 1 4 FALSE FALSE
#> 4 D3 STRING8 8 1 8 FALSE FALSE
#> 5 D4 STRING16 16 NA NA FALSE FALSE
#> 6 D5 STRING32 32 1 32 FALSE FALSE
#> 7 D6 STRING64 64 1 64 FALSE FALSE
#> 8 D7 STRING256 256 1 256 FALSE FALSE
#> 9 D9 N_PARAM 7 1 7 FALSE FALSE
#> 10 D11 N_CALIB 1 1 1 FALSE FALSE
#> 11 D12 N_HISTORY 0 NA NA TRUE FALSE
#> 12 D13 N_VALUES41 41 NA NA FALSE FALSE
argo %>% hyper_tbl_cube()
#> Source: local array [986 x 2]
#> D: N_LEVELS [int, 493]
#> D: N_PROF [int, 2]
#> M: PRES [dbl[,2]]
#> M: PRES_QC [chr[,2]]
#> M: PRES_ADJUSTED [dbl[,2]]
#> M: PRES_ADJUSTED_QC [chr[,2]]
#> M: PRES_ADJUSTED_ERROR [dbl[,2]]
#> M: TEMP [dbl[,2]]
#> M: TEMP_QC [chr[,2]]
#> M: TEMP_ADJUSTED [dbl[,2]]
#> M: TEMP_ADJUSTED_QC [chr[,2]]
#> M: TEMP_ADJUSTED_ERROR [dbl[,2]]
#> M: PSAL [dbl[,2]]
#> M: PSAL_QC [chr[,2]]
#> M: PSAL_ADJUSTED [dbl[,2]]
#> M: PSAL_ADJUSTED_QC [chr[,2]]
#> M: PSAL_ADJUSTED_ERROR [dbl[,2]]
#> M: DOXY [dbl[,2]]
#> M: DOXY_QC [chr[,2]]
#> M: DOXY_ADJUSTED [dbl[,2]]
#> M: DOXY_ADJUSTED_QC [chr[,2]]
#> M: DOXY_ADJUSTED_ERROR [dbl[,2]]
#> M: CHLA [dbl[,2]]
#> M: CHLA_QC [chr[,2]]
#> M: CHLA_ADJUSTED [dbl[,2]]
#> M: CHLA_ADJUSTED_QC [chr[,2]]
#> M: CHLA_ADJUSTED_ERROR [dbl[,2]]
#> M: BBP700 [dbl[,2]]
#> M: BBP700_QC [chr[,2]]
#> M: BBP700_ADJUSTED [dbl[,2]]
#> M: BBP700_ADJUSTED_QC [chr[,2]]
#> M: BBP700_ADJUSTED_ERROR [dbl[,2]]
#> M: NITRATE [dbl[,2]]
#> M: NITRATE_QC [chr[,2]]
#> M: NITRATE_ADJUSTED [dbl[,2]]
#> M: NITRATE_ADJUSTED_QC [chr[,2]]
#> M: NITRATE_ADJUSTED_ERROR [dbl[,2]]
argo %>% hyper_tibble(select_var = c("TEMP_QC"))
#> # A tibble: 986 × 3
#> TEMP_QC N_LEVELS N_PROF
#> <chr> <chr> <chr>
#> 1 1 1 1
#> 2 1 2 1
#> 3 1 3 1
#> 4 1 4 1
#> 5 1 5 1
#> 6 1 6 1
#> 7 1 7 1
#> 8 1 8 1
#> 9 1 9 1
#> 10 1 10 1
#> # ℹ 976 more rows
argo %>% hyper_transforms()
#> $N_LEVELS
#> # A tibble: 493 × 6
#> N_LEVELS index id name coord_dim selected
#> <int> <int> <int> <chr> <lgl> <lgl>
#> 1 1 1 10 N_LEVELS FALSE TRUE
#> 2 2 2 10 N_LEVELS FALSE TRUE
#> 3 3 3 10 N_LEVELS FALSE TRUE
#> 4 4 4 10 N_LEVELS FALSE TRUE
#> 5 5 5 10 N_LEVELS FALSE TRUE
#> 6 6 6 10 N_LEVELS FALSE TRUE
#> 7 7 7 10 N_LEVELS FALSE TRUE
#> 8 8 8 10 N_LEVELS FALSE TRUE
#> 9 9 9 10 N_LEVELS FALSE TRUE
#> 10 10 10 10 N_LEVELS FALSE TRUE
#> # ℹ 483 more rows
#>
#> $N_PROF
#> # A tibble: 2 × 6
#> N_PROF index id name coord_dim selected
#> <int> <int> <int> <chr> <lgl> <lgl>
#> 1 1 1 8 N_PROF FALSE TRUE
#> 2 2 2 8 N_PROF FALSE TRUE
#>
argo %>% hyper_vars()
#> # A tibble: 35 × 6
#> id name type ndims natts dim_coord
#> <int> <chr> <chr> <int> <int> <lgl>
#> 1 37 PRES NC_FLOAT 2 10 FALSE
#> 2 38 PRES_QC NC_CHAR 2 3 FALSE
#> 3 39 PRES_ADJUSTED NC_FLOAT 2 9 FALSE
#> 4 40 PRES_ADJUSTED_QC NC_CHAR 2 3 FALSE
#> 5 41 PRES_ADJUSTED_ERROR NC_FLOAT 2 6 FALSE
#> 6 42 TEMP NC_FLOAT 2 9 FALSE
#> 7 43 TEMP_QC NC_CHAR 2 3 FALSE
#> 8 44 TEMP_ADJUSTED NC_FLOAT 2 9 FALSE
#> 9 45 TEMP_ADJUSTED_QC NC_CHAR 2 3 FALSE
#> 10 46 TEMP_ADJUSTED_ERROR NC_FLOAT 2 6 FALSE
#> # ℹ 25 more rows
argo %>% hyper_dims()
#> # A tibble: 2 × 7
#> name length start count id unlim coord_dim
#> <chr> <dbl> <int> <int> <int> <lgl> <lgl>
#> 1 N_LEVELS 493 1 493 10 FALSE FALSE
#> 2 N_PROF 2 1 2 8 FALSE FALSE
argo %>% hyper_grids()
#> # A tibble: 16 × 4
#> grid ndims nvars active
#> <chr> <int> <int> <lgl>
#> 1 D0,D9,D11,D8 4 1 FALSE
#> 2 D6,D9,D11,D8 4 1 FALSE
#> 3 D7,D9,D11,D8 4 3 FALSE
#> 4 D6,D9,D8 3 1 FALSE
#> 5 D10,D8 2 35 TRUE
#> 6 D1,D8 2 1 FALSE
#> 7 D2,D8 2 2 FALSE
#> 8 D3,D8 2 2 FALSE
#> 9 D5,D8 2 4 FALSE
#> 10 D6,D8 2 2 FALSE
#> 11 D7,D8 2 1 FALSE
#> 12 D9,D8 2 1 FALSE
#> 13 D0 1 3 FALSE
#> 14 D2 1 2 FALSE
#> 15 D5 1 1 FALSE
#> 16 D8 1 17 FALSE
## some global options
getOption("tidync.large.data.check")
#> [1] TRUE
getOption("tidync.silent")
#> [1] FALSE
op <- options(tidync.silent = TRUE)
getOption("tidync.silent")
#> [1] TRUE
options(op)