---
title: "Getting started with chopin"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting started with chopin}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


```{r, include = FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
```

## Introduction
- `chopin` automatically distributes geospatial data computation over multiple threads.
- Function centered workflow will streamline the process of parallelizing geospatial computation with minimal effort.


## `chopin` workflow
- The simplest way of parallelizing generic geospatial computation is to start from `par_pad_*` functions to `par_grid`, or running `par_hierarchy`, or `par_multirasters` functions at once.

```{r}
library(chopin)
library(terra)
library(sf)
library(collapse)
library(dplyr)
library(future)
library(future.mirai)
library(future.apply)
```


## Example data
- North Carolinian counties and a raster of elevation data are used as example data.

```{r}
nccnty_path <- system.file("extdata", "nc_hierarchy.gpkg", package = "chopin")
ncelev_path <-
  system.file("extdata/nc_srtm15_otm.tif", package = "chopin")
nccnty <- terra::vect(nccnty_path)

ncelev <- terra::rast(ncelev_path)

```

### Generating random points in North Carolina
- To demonstrate chopin functions, we generate 10,000 random points in North Carolina

```{r}
ncsamp <-
  terra::spatSample(
    nccnty,
    1e4L
  )
ncsamp$pid <- 1:nrow(ncsamp)
```

## Creating grids
- The example below will generate a regular grid from the random point data.

```{r}
ncgrid <- par_pad_grid(ncsamp, mode = "grid", nx = 4L, ny = 2L, padding = 10000)

plot(ncgrid$original)
```



## Extracting values from raster
- Since all `par_*` functions operate on `future` backends, users should define the future plan before running the functions. `multicore` plan supports `terra` objects which may lead to faster computation, but it is not supported in Windows. An alternative is `future.mirai`'s `mirai_multisession` plan, which is supported in many platforms and generally faster than plain future multisession plan.
- `workers` argument should be defined with an integer value to specify the number of threads to be used.

```{r}
future::plan(future.mirai::mirai_multisession, workers = 2L)
```

- Then we dispatch multiple `extract_at` runs on the grid polygons.
- Before we proceed, the terra object should be converted to sf object.

```{r}

pg <-
  par_grid(
    grids = ncgrid,
    pad_y = FALSE,
    .debug = TRUE,
    fun_dist = extract_at,
    x = ncelev_path,
    y = sf::st_as_sf(ncsamp),
    id = "pid",
    radius = 1e4,
    func = "mean"
  )

```


## Hierarchical processing
- Here we demonstrate hierarchical processing of the random points using census tract polygons.

```{r}
nccnty <- sf::st_read(nccnty_path, layer = "county")
nctrct <- sf::st_read(nccnty_path, layer = "tracts")

```

- The example below will parallelize summarizing mean elevation at 10 kilometers circular buffers of random sample points by the first five characters of census tract unique identifiers, which are county codes.
- This example demonstrates the hierarchy can be defined from any given polygons if the unique identifiers are suitably formatted for defining the hierarchy.

```{r}
px <-
  par_hierarchy(
    # from here the par_hierarchy-specific arguments
    regions = nctrct,
    regions_id = "GEOID",
    length_left = 5,
    pad = 10000,
    pad_y = FALSE,
    .debug = TRUE,
    # from here are the dispatched function definition
    # for parallel workers
    fun_dist = extract_at,
    # below should follow the arguments of the dispatched function
    x = ncelev,
    y = sf::st_as_sf(ncsamp),
    id = "pid",
    radius = 1e4,
    func = "mean"
  )

dim(px)
head(px)
tail(px)
```

## Multiraster processing
- Here we demonstrate multiraster processing of the random points using multiple rasters.

```{r}
ncelev <-
  system.file("extdata/nc_srtm15_otm.tif", package = "chopin")
ncelev <- terra::rast(ncelev)
tdir <- tempdir(check = TRUE)
terra::writeRaster(ncelev, file.path(tdir, "test1.tif"), overwrite = TRUE)
terra::writeRaster(ncelev, file.path(tdir, "test2.tif"), overwrite = TRUE)
terra::writeRaster(ncelev, file.path(tdir, "test3.tif"), overwrite = TRUE)
terra::writeRaster(ncelev, file.path(tdir, "test4.tif"), overwrite = TRUE)
terra::writeRaster(ncelev, file.path(tdir, "test5.tif"), overwrite = TRUE)

rasts <- list.files(tdir, pattern = "tif$", full.names = TRUE)

pm <-
  par_multirasters(
    filenames = rasts,
    fun_dist = extract_at,
    x = NA,
    y = sf::st_as_sf(ncsamp)[1:500, ],
    id = "pid",
    radius = 1e4,
    func = "mean",
    .debug = TRUE
  )

dim(pm)
head(pm)
tail(pm)
```


## Caveats

### Why parallelization is slower than the ordinary function run?
- Parallelization may underperform when the datasets are too small to take advantage of divide-and-compute approach, where parallelization overhead is involved. Overhead here refers to the required amount of computational resources for transferring objects to multiple processes.
- Since the demonstrations above use quite small datasets, the advantage of parallelization was not as noticeable as it was expected. Should a large amount of data (spatial/temporal resolution or number of files, for example) be processed, users could find the efficiency of this package. A vignette in this package demonstrates use cases extracting various climate/weather datasets.


### Notes on data restrictions
`chopin` works best with **two-dimensional** (**planar**) geometries. Users should disable `s2` spherical geometry mode in `sf` by setting `sf::sf_use_s2(FALSE)`. Running any `chopin` functions at spherical or three-dimensional (e.g., including M/Z dimensions) geometries may produce incorrect or unexpected results.


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