---
title: "2. stars proxy objects"
author: "Edzer Pebesma"
output:
  html_document:
    toc: true
    toc_float:
      collapsed: false
      smooth_scroll: false
    toc_depth: 2
vignette: >
  %\VignetteIndexEntry{2. stars proxy objects}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png")
set.seed(13579)
EVAL = x = suppressWarnings(require(starsdata, quietly = TRUE))
```

When your imagery or array data easily fits a couple of times in
R's working memory (RAM), consider yourself lucky. This document
was not written for you.  If your imagery is too large, or for
other reasons you want to work with smaller chunks of data than
the files in which they come, read on about your options.
First, we will discuss the low-level interface for this, then
the higher level, using stars proxy objects that delay all reading.

# Preamble: the starsdata package

To run all of the examples in this vignette, you must install a package with datasets that are too large (1 Gb) to be held in the `stars` package. They are in a [drat repo](https://github.com/eddelbuettel/drat), installation is done by
```{r eval=FALSE}
install.packages("starsdata", repos = "http://gis-bigdata.uni-muenster.de/pebesma", type = "source") 
# possibly after: options(timeout = 100)
# or from an alternative repository:
# install.packages("starsdata", repos = "http://pebesma.staff.ifgi.de", type = "source")
```

# Reading chunks, change resolution, select bands

`read_stars` has an argument called `RasterIO` which controls how
a GDAL dataset is being read. By default, all pixels and all bands
are read in memory. This can consume a lot of time and require
a lot of memory. Remember that your file may be compressed, and
that pixel values represented in the file by bytes are converted
to 8-byte doubles in R. 

The reason for using `RasterIO` for this is that the parameters we
use are directly mapped to the GDAL RasterIO function used (after
adapting the 1-based offset index in R to 0-based offset in C++).

## Reading a particular chunk 

<!-- We can read a (spatially) rectangular chunk of data by -->

An example of using `RasterIO` is
```{r}
library(stars)
tif = system.file("tif/L7_ETMs.tif", package = "stars")
rasterio = list(nXOff = 6, nYOff = 6, nXSize = 100, nYSize = 100, bands = c(1, 3, 4))
(x = read_stars(tif, RasterIO = rasterio))
dim(x)
```
Compare this to
```{r}
st_dimensions(read_stars(tif))
```
and we see that

* the `delta` values remain the same,
* the offset (x/y coordinates of origin) of the grid remain the same
* the `from` and `to` reflect the new area, and relate to the new `delta` values
* `dim(x)` reflects the new size, and
* only three bands were read

## Reading at a different resolution

Reading datasets at a lower (but also higher!) resolution can be
done by setting `nBufXSize` and `nBufYSize`

```{r}
rasterio = list(nXOff = 6, nYOff = 6, nXSize = 100, nYSize = 100,
                nBufXSize = 20, nBufYSize = 20, bands = c(1, 3, 4))
(x = read_stars(tif, RasterIO = rasterio))
```
and we see that in addition:

* the `delta` (raster cell size) values have increased a factor 5, because `nBufXSize` and `nBufYSize` were set to values a factor 5 smaller than `nXSize` and `nYSize`
* the offset coordinates of the grid are still the same
* the `from` and `to` reflect the new area, but relate to the new `delta` cell size values

We can also read at higher resolution; here we read a 3 x 3 area and blow it up to 100 x 100:
```{r}
rasterio = list(nXOff = 6, nYOff = 6, nXSize = 3, nYSize = 3,
   nBufXSize = 100, nBufYSize = 100, bands = 1)
x = read_stars(tif, RasterIO = rasterio)
dim(x)
plot(x)
```

The reason we "see" only three grid cells is that the default sampling method is "nearest neighbour". We can modify this by

```{r}
rasterio = list(nXOff = 6, nYOff = 6, nXSize = 3, nYSize = 3,
   nBufXSize = 100, nBufYSize = 100, bands = 1, resample = "cubic_spline")
x = read_stars(tif, RasterIO = rasterio)
dim(x)
plot(x)
```

The following methods are allowed for parameter `resample`:

| `resample` | method used                        |
|------------|------------------------------------|
|`nearest_neighbour`|   Nearest neighbour (default) |
|`bilinear` |      Bilinear (2x2 kernel) |
|`cubic`     |  Cubic Convolution Approximation (4x4 kernel) |
|`cubic_spline` |  Cubic B-Spline Approximation (4x4 kernel) |
|`lanczos` |  Lanczos windowed sinc interpolation (6x6 kernel) |
|`average` |  Average |
|`mode` |  Mode (selects the value which appears most often of all the sampled points) |
|`Gauss` |  Gauss blurring |

All these methods are implemented in GDAL; for what these methods exactly do, we refer to the GDAL documentation or source code.

# Stars proxy objects

Stars proxy objects take another approach: upon creation they contain no data at all, but only pointers to where the data can be read. Data is only read when it is needed, and only as much as is needed: if we plot a proxy objects, the data are read at the resolution of pixels on the screen, rather than at the native resolution, so that if we have e.g. a 10000 x 10000 Sentinel 2 (level 1C) image, we can open it by
```{r, eval=EVAL}
granule = system.file("sentinel/S2A_MSIL1C_20180220T105051_N0206_R051_T32ULE_20180221T134037.zip", package = "starsdata")
s2 = paste0("SENTINEL2_L1C:/vsizip/", granule, "/S2A_MSIL1C_20180220T105051_N0206_R051_T32ULE_20180221T134037.SAFE/MTD_MSIL1C.xml:10m:EPSG_32632")
(p = read_stars(s2, proxy = TRUE))
```
and this happens _instantly_, because no data is read. When we plot this object,
```{r,eval=EVAL}
system.time(plot(p))
```

This takes only around 1 second, since only those pixels are read that can be seen on the plot. If we read the entire image in memory first, as we would do with
```{r eval = FALSE}
p = read_stars(s2, proxy = FALSE)
```
then only the reading would take over a minute, and require 5 Gb memory.

## Methods for stars proxy objects

```{r}
methods(class = "stars_proxy")
```

## Select attributes

We can select attributes as with regular `stars` objects, by using the first argument to `[`:
```{r,eval=EVAL}
x = c("avhrr-only-v2.19810901.nc",
"avhrr-only-v2.19810902.nc",
"avhrr-only-v2.19810903.nc",
"avhrr-only-v2.19810904.nc",
"avhrr-only-v2.19810905.nc",
"avhrr-only-v2.19810906.nc",
"avhrr-only-v2.19810907.nc",
"avhrr-only-v2.19810908.nc",
"avhrr-only-v2.19810909.nc")
file_list = system.file(paste0("netcdf/", x), package = "starsdata")
y = read_stars(file_list, quiet = TRUE, proxy = TRUE)
names(y)
y["sst"]
```
Note that this selection limits the reading from 4 to 1 subdataset from all 9 NetCDF files.

## Select an area

Another possibility is to crop, or select a rectangular region based on a spatial object. This can be done by passing a `bbox` object, or an `sf`, `sfc` or `stars` object from which the bounding box will be taken. An example:

```{r,eval=EVAL}
bb = st_bbox(c(xmin = 10.125, ymin = 0.125, xmax = 70.125, ymax = 70.125))
ysub = y[bb]
st_dimensions(ysub)
class(ysub) # still no data here!!
plot(ysub, reset = FALSE) # plot reads the data, at resolution that is relevant
plot(st_as_sfc(bb), add = TRUE, lwd = .5, border = 'red')
```

## Lazy evaluation, changing evaluation order

Some other actions can be carried out on `stars_proxy` objects, but their effect is delayed until the data are actually needed (`plot`, `write_stars`).  For instance, range selections on dimensions other than shown above first need data, and can only then be carried out. Such functions are added to the object, in an attribute called `call_list`:

```{r,eval=EVAL}
yy = adrop(y)
yyy = yy[,1:10,1:10,]
class(yyy) # still no data
st_dimensions(yyy) # and dimensions not adjusted
attr(yyy, "call_list") # the name of object in the call (y) is replaced with x:
```

Doing this allows for optimizing the order in which operations are done. As an example, for `st_apply`, reading can be done sequentially over the dimensions over which the function is applied: 

* If for example a function is applied to each band (such as: compute band quantiles), bands can be read sequentially, and discarded after the quantiles have been computed. 
* If a time series function is applied to pixel time series and the result is plotted on a map, the time series function is only evaluated on the pixels actually plotted. This means that e.g. in
```{r eval = FALSE}
plot(st_apply(x, c("x", "y"), range))
```
the order of evaluation is reversed:  `plot` knows which pixels are going to be shown, and controls how `x` is downsampled *before* `st_apply` is carried out on this subset.


### Fetching the data

Fetching the data now involves reading the whole array and then evaluating the `call_list` on it, sequentially:
```{r,eval=EVAL}
(x = st_as_stars(yyy)) # read, adrop, subset
```

### Plotting with changed evaluation order

For the Sentinel 2 data, band 4 represents NIR and band 1 red, so we can compute NDVI by
```{r,eval=EVAL}
# S2 10m: band 4: near infrared, band 1: red.
#ndvi = function(x) (x[4] - x[1])/(x[4] + x[1])
ndvi = function(x1, x2, x3, x4) (x4 - x1)/(x4 + x1)
rm(x)
(s2.ndvi = st_apply(p, c("x", "y"), ndvi))
system.time(plot(s2.ndvi)) # read - compute ndvi - plot 
```

# Multi-resolution proxy objects

This sections shows some examples how `stars_proxy` objects deal with the situation where the different maps have dissimilar resolution. The assumptions here are:

* all maps need to have the same origin coordinates (typically upper-left corner) and CRS.
* the first map determines the "working" resolution, to which e.g. native or downsampled resolutions refer

We'll create four maps with cells size 1, 2 and 3:
```{r}
s1 = st_as_stars(matrix(1:16, 4))
s2 = st_as_stars(matrix(1:16, 4))
s3 = st_as_stars(matrix(1:16, 4))
attr(s1, "dimensions")$X1$offset = 0
attr(s1, "dimensions")$X2$offset = 4
attr(s2, "dimensions")$X1$offset = 0
attr(s2, "dimensions")$X2$offset = 4
attr(s3, "dimensions")$X1$offset = 0
attr(s3, "dimensions")$X2$offset = 4
attr(s1, "dimensions")$X1$delta =  1
attr(s1, "dimensions")$X2$delta = -1
attr(s2, "dimensions")$X1$delta =  2
attr(s2, "dimensions")$X2$delta = -2
attr(s3, "dimensions")$X1$delta =  3
attr(s3, "dimensions")$X2$delta = -3
plot(s1, axes = TRUE, text_values = TRUE, text_color = 'orange')
plot(s2, axes = TRUE, text_values = TRUE, text_color = 'orange')
plot(s3, axes = TRUE, text_values = TRUE, text_color = 'orange')
```

We created three rasters with identical cell values and dimensions, but different cell sizes, and hence extents. If we bind them in a single proxy object, with

```{r eval=TRUE}
fn1 = paste0(tempdir(), .Platform$file.sep, "img1.tif")
fn2 = paste0(tempdir(), .Platform$file.sep, "img2.tif")
fn3 = paste0(tempdir(), .Platform$file.sep, "img3.tif")
write_stars(s1, fn1)
write_stars(s2, fn2)
write_stars(s3, fn3) 
(r1 = read_stars(c(fn1, fn2, fn3), proxy = TRUE))
```

We see that **multi-resolution** is mentioned in the printed summary. When converting this to a `stars` object, the secondary rasters are resampled to the cellsize + extent of the first:

```{r eval=TRUE}
st_as_stars(r1) %>%
  merge() %>%
  plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE)
```

If we do this for a sub-range, defined for the object resolutions, we get:
```{r eval=TRUE}
st_as_stars(r1[,2:4,2:4]) %>%
  merge() %>%
  plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE)
```

We now create four maps, all over the same region ([0,4] x [0,4]), with different resolutions (cell size 1, 1/2 and 1/3): 
```{r}
s4 = st_as_stars(matrix(1: 16, 4))
s5 = st_as_stars(matrix(1: 64, 8))
s6 = st_as_stars(matrix(1:144,12))
attr(s4, "dimensions")$X1$offset = 0
attr(s4, "dimensions")$X2$offset = 4
attr(s5, "dimensions")$X1$offset = 0
attr(s5, "dimensions")$X2$offset = 4
attr(s6, "dimensions")$X1$offset = 0
attr(s6, "dimensions")$X2$offset = 4
attr(s4, "dimensions")$X1$delta =  1
attr(s4, "dimensions")$X2$delta = -1
attr(s5, "dimensions")$X1$delta =  1/2
attr(s5, "dimensions")$X2$delta = -1/2
attr(s6, "dimensions")$X1$delta =  1/3
attr(s6, "dimensions")$X2$delta = -1/3
plot(s4, axes = TRUE, text_values = TRUE, text_color = 'orange')
plot(s5, axes = TRUE, text_values = TRUE, text_color = 'orange')
plot(s6, axes = TRUE, text_values = TRUE, text_color = 'orange')
```

```{r eval=TRUE}
fn4 = paste0(tempdir(), .Platform$file.sep, "img4.tif")
fn5 = paste0(tempdir(), .Platform$file.sep, "img5.tif")
fn6 = paste0(tempdir(), .Platform$file.sep, "img6.tif")
write_stars(s4, fn4)
write_stars(s5, fn5)
write_stars(s6, fn6) 
(r2 = read_stars(c(fn4, fn5, fn6), proxy = TRUE))

st_as_stars(r2) %>%
  merge() %>%
  plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE)
st_as_stars(r2[,2:4,2:4]) %>%
  merge() %>%
  plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE)
```

Finally, an example where the first raster has the higher resolution:

```{r eval=TRUE}
(r3 = read_stars(c(fn6, fn5, fn4), proxy = TRUE))

st_as_stars(r3) %>%
  merge() %>%
  plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE)
st_as_stars(r3[,2:6,3:6]) %>%
  merge() %>%
  plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE)
```
