itsalive.R

## use ncdump to treat NetCDF like a virtual DB
library(ncdf4)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ncdump)
f <- "data/eclipse.ncdc.noaa.gov/pub/OI-daily-v2/NetCDF/2017/AVHRR/avhrr-only-v2.20170502_preliminary.nc"
x <- NetCDF(f)
x

## character(0)
## [1] "(sst, anom, err, ice)"
## [1] "Dimension:"
##   name length unlimited
## 1 time      1     FALSE
## 2 zlev      1     FALSE
## 3  lat    720     FALSE
## 4  lon   1440     FALSE

nctive(x)

## NULL

## push sst to the front for an extraction
x <- nctivate(x, "sst")
hyper_slab <- filtrate(x, lon = lon > 100, lat = lat < 30)

## Joining, by = "id"

## Joining, by = "id"
## Joining, by = "id"

## it's alive, test the extraction
nc <- nc_open(f)

var <- ncvar_get(nc, "sst", start = bind_rows(hyper_slab)$start, count = bind_rows(hyper_slab)$count)
image(var)

## push a different var to the front
x <- nctivate(x, "anom")
hyper_slab <- filtrate(x, lon = between(lon, 50, 147), lat = between(lat, -42, 20))

## Joining, by = "id"
## Joining, by = "id"
## Joining, by = "id"

var <- ncvar_get(nc, "sst", start = bind_rows(hyper_slab)$start, count = bind_rows(hyper_slab)$count)
image(var)

hyper_slab

## $lon
## # A tibble: 1 × 3
##    name start count
##   <chr> <dbl> <int>
## 1   lon   201   388
## 
## $lat
## # A tibble: 1 × 3
##    name start count
##   <chr> <dbl> <int>
## 1   lat   193   248
## 
## $zlev
## # A tibble: 1 × 3
##    name start count
##   <chr> <dbl> <int>
## 1  zlev     1     1
## 
## $time
## # A tibble: 1 × 3
##    name start count
##   <chr> <dbl> <int>
## 1  time     1     1