---
title: "Legacy data formats with cloud tools"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Legacy data formats with cloud tools}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

Not all data formats which NASA EarthData makes available on cloud storage are provided in cloud-optimized or even VSI-compatible formats.  A stark example is HDF4. 
Here we illustrate how to work with these data formats while staying as close to the cloud-native workflow shown in other vignettes.

Aside: note that in many cases, these same data products may be available in cloud-native formats from other providers.  Particular to the example here, see the cloud-optimized [MODIS catalog on the Planetary Computer STAC](https://planetarycomputer.microsoft.com/dataset/group/modis).





```r
library(earthdatalogin)
```



```r
library(rstac)
library(gdalcubes)
library(spData)
library(sf)
gdalcubes_options(parallel = TRUE) 
```


```r
CA <- spData::us_states |> dplyr::filter(NAME=="California")
bbox <- CA |> st_bbox()
start <- "2022-01-01"
end <- "2022-12-31"

items <- stac("https://cmr.earthdata.nasa.gov/stac/LPCLOUD") |> 
  stac_search(collections = "MOD13Q1.v061",
              bbox = c(bbox),
              datetime = paste(start,end, sep = "/")) |>
  post_request() |>
  items_fetch()
#> 
  |                                                                                                                                                                                                                                                                
  |==========================================================================================================================================================================================================================================================| 100%
```

HDF4 doesn't support cloud (range-request-based) access / VSI.
Instead, we download all matching assets with `earthdatalogin`
authentication:


```r
paths <- items$features |>
  purrr::map(list("assets", "data", "href")) |> 
  unlist() |>
  purrr::map_chr(edl_download)
```

Rather than create an image collection using STAC metadata, we can use the recognized format and the local paths:


```r
paths <- fs::dir_ls(".", glob="*.hdf")
col <- gdalcubes::create_image_collection(paths, format = "MxD13Q1")
```


```r
# Define whatever view you like!
v = cube_view(srs = "EPSG:4326",
              extent = list(t0 = as.character(start), 
                            t1 = as.character(end),
                            left = bbox[1], right = bbox[3],
                            top = bbox[4], bottom = bbox[2]),
              nx = 512, ny = 512, dt = "P1M")
```


```r
raster_cube(col, v) |> 
  select_bands("NDVI") |> 
  animate(col = viridisLite::mako, fps=2, 
          save_as="img/ndvi.gif")
#> [1] "/home/cboettig/boettiger-lab/earthdatalogin/inst/vignette-sources/img/ndvi.gif"
```

![](img/ndvi.gif)


```r
library(tmap)
r <- 
  raster_cube(col, v) |> 
  select_bands("NDVI") |> 
  st_as_stars.cube()

tm_shape(r) + tm_raster("NDVI") +
  tm_shape(CA) + tm_borders()
```

![plot of chunk india_ndvi_modis](img/india_ndvi_modis-1.png)


