---
title: "8. NetCDF Proxy Workflows"
author: "David Blodgett"
output:
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    toc: true
    toc_float:
      collapsed: false
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    toc_depth: 2
vignette: >
  %\VignetteIndexEntry{8. NetCDF Proxy Workflows}
  %\VignetteEngine{knitr::rmarkdown}
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---

```{r, include = FALSE}
knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png")
suppressPackageStartupMessages(library(dplyr))
EVAL = suppressWarnings(require(starsdata, quietly = TRUE))
```

NetCDF data sources are available via more _and_ less granular files and/or OPeNDAP endpoints. This article demonstrates how `stars` enables discovery, access, and processing of NetCDF data across a wide range of such source-data organization schemes.

We'll start with some basics using datasets included with the `stars` installation. A call to `read_ncdf()`, for a dataset smaller than the default threshold, will just read in all the data. Below we read in and display the `reduced.nc` NetCDF file.

```{r}
library(stars)
f <- system.file("nc/reduced.nc", package = "stars")
(nc <- read_ncdf(f))
```

Let's assume `reduced.nc` was 10 years of hourly data, rather than 1 time step. It would be over 10GB rather than about 130KB and we would not be able to just read it all into memory. In this case, we need a way to read the file's metadata such that we could iterate over it in a way that meets the needs of our workflow objectives. This is where `proxy = TRUE` comes in. Below, we'll lower the option that controls whether `read_ncdf()` defaults to proxy _and_ use `proxy = TRUE` to show both ways of getting the same result.

```{r}
old_options <- options("stars.n_proxy" = 100)
(nc <- read_ncdf(f, proxy = TRUE))
options(old_options)
```

The above shows that we have a NetCDF sourced stars proxy derived from the `reduced.nc` file. We see it has four variables and their units are displayed. The normal `stars` `dimension(s)` are available and a `nc_request` object is also available. The `nc_request` object contains the information needed to make requests for data according to the dimensions of the NetCDF data source. With this information, we have what we need to request a chunk of data that is what we want and not too large.

```{r}
(nc <- read_ncdf(f, 
				 var = "sst", 
				 ncsub = cbind(start = c(90, 45, 1 , 1), 
				 			  count = c(90, 45, 1, 1))))

plot(nc)
```

The ability to view NetCDF metadata so we can make well formed requests against the data is useful, but the real power of a proxy object is that we can use it in a "lazy evaluation" coding style. That is, we can do virtual operations on the object, like subsetting with another dataset, prior to actually accessing the data volume. 

Lazy operations.

There are two kinds of lazy operations possible with `stars_proxy` objects. Some can be applied to the `stars_proxy` object itself without accessing underlying data. Others must be composed as a chain of calls that will be applied when data is actually required. 

Methods applied to a `stars_proxy` object:

- `[` - Nearly the same as stars_proxy
- `[[<-` - stars_proxy method works
- `print` - unique method for nc_proxy to facilitate unique workflows
- `dim` - stars_proxy method works
- `c` - stars_proxy method works
- `st_redimension` - Not sure what this entails but it might not make sense for nc_proxy.
- `st_mosaic` * Calls read_stars on assembled list. Not supported for now.
- `st_set_bbox`


Methods that add a call to the `call_list`.

- `[<-`
- `adrop`
- `aperm`
- `is.na`
- `split`
- `st_apply`
- `predict`
- `merge`
- `st_crop`
- `drop_levels`
- `Ops` (group generic for +, -, etc.)
- `Math` (group generic for abs, sqrt, tan, etc.)
- `filter`
- `mutate`
- `tansmute`
- `select`
- `rename`
- `pull`
- `slice` * hyperslabbing for NetCDF could be as above?
- `pull`
- `replace_na`

Methods that cause a `stars_proxy` object to be fetched and turned into a `stars` object.

- `as.data.frame`
- `plot`
- `st_as_stars`
- `aggregate`
- `st_dimensions<-` * https://github.com/r-spatial/stars/issues/494
- `hist`
- `st_downsample`
- `st_sample`
- `st_as_sf`
- `write_stars`


