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{stars} functions for raster data cube and post object interaction, including time matching #1

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loreabad6 opened this issue Sep 18, 2024 · 0 comments
Open

{stars} functions for raster data cube and post object interaction, including time matching #1

loreabad6 opened this issue Sep 18, 2024 · 0 comments

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@loreabad6
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loreabad6 commented Sep 18, 2024 •
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Having a better interaction between post_array and stars objects with raster data would greatly benefit the enrichment of post objects from raster information.
Currently, the majority of functions do work when giving a sf object, which is then easier to do when passing a post_table in long form, but passing a post_array would be the most intuitive way, specially if the we can work with the dimensions attribute.

Also, enabling time matching for all these functions would be a great way to perform the operations in a "spatio-temporal"-aware manner.

Dummy raster data cube (rdc)

remotes::install_github("loreabad6/stars@time-polygon")
#> Skipping install of 'stars' from a github remote, the SHA1 (f70aca5e) has not changed since last install.
#>   Use `force = TRUE` to force installation
library(stars)
library(sf)
library(post)
library(cubble)
library(tidyverse)

x = seq(-0.5, 1, length.out = 20)
y = seq(0, 1.5, length.out = 20)
s1 = st_as_stars(data.frame(expand.grid(x = x, y = y), z = rnorm(400)))
s2 = st_as_stars(data.frame(expand.grid(x = x, y = y), z = rnorm(400)))
s3 = st_as_stars(data.frame(expand.grid(x = x, y = y), z = rnorm(400)))
s4 = st_as_stars(data.frame(expand.grid(x = x, y = y), z = rnorm(400)))
s5 = st_as_stars(data.frame(expand.grid(x = x, y = y), z = rnorm(400)))
rdc = c(s1,s2,s3,s4,s5, along = "datetime") |> 
  st_set_dimensions(
    "datetime",
    values = unique(polygons$datetime),
  ) |> 
  st_set_crs(4326)

arr = polygons |> as_post_array()
tab = polygons |> as_post_table() |> face_temporal()

1. stars::aggregate.stars()

  • We can pass the polygons from the temporal face of a post_table to aggregate().stars.
  • The way the geometries are then organised within the array is unclear, should be looked into.
  • This should also work for post_array geometries.
  • Including time-matching already at the aggregation level might be beneficial to avoid aggregating on all the geometries as it works in st_extract()
(agg = aggregate(rdc, tab, mean, na.rm = TRUE))
#> stars object with 2 dimensions and 1 attribute
#> attribute(s):
#>         Min.    1st Qu.     Median       Mean   3rd Qu.     Max. NA's
#> z  -1.547645 -0.2601401 0.09971962 0.08011473 0.3912051 1.321302   35
#> dimension(s):
#>          from to     offset  delta refsys point
#> geometry    1 25         NA     NA WGS 84 FALSE
#> datetime    1  5 2020-10-01 1 days   Date    NA
#>                                                                 values
#> geometry POLYGON ((0.5474949 0.808...,...,POLYGON ((0.4750757 0.262...
#> datetime                                                          NULL
st_as_sf(agg)
#> Simple feature collection with 25 features and 5 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -0.2974337 ymin: -0.00297557 xmax: 0.9730806 ymax: 1.153558
#> Geodetic CRS:  WGS 84
#> First 10 features:
#>     2020-10-01   2020-10-02  2020-10-03  2020-10-04 2020-10-05
#> 1  -0.71838423  0.692081576  0.08165758 -0.70646498 -0.2634729
#> 2  -0.30162972 -0.002314025  0.12940001  0.18564799  0.2301074
#> 3   1.32130231  1.016536556  0.26029296  0.87806310  0.4007806
#> 4           NA           NA          NA          NA         NA
#> 5  -1.10219328 -0.250141796  0.24053519  0.40932174  0.2253243
#> 6  -0.07874849  0.153725017 -0.43001741  0.08132226  0.2510174
#> 7   0.14342283 -0.437820714  0.06228408  0.09273263  0.1770414
#> 8           NA           NA          NA          NA         NA
#> 9           NA           NA          NA          NA         NA
#> 10  0.83302258  0.144860774 -0.02748157  0.19337529  0.6889722
#>                          geometry
#> 1  POLYGON ((0.5474949 0.80889...
#> 2  POLYGON ((0.4961102 0.87283...
#> 3  POLYGON ((0.5578801 0.86163...
#> 4  POLYGON ((0.5652241 0.87205...
#> 5  POLYGON ((0.6063791 0.83041...
#> 6  POLYGON ((0.2791708 0.83373...
#> 7  POLYGON ((0.3298312 0.76120...
#> 8  POLYGON ((0.3796448 0.76785...
#> 9  POLYGON ((0.3642467 0.77972...
#> 10 POLYGON ((0.2665368 0.86499...

2. stars::st_extract()

  • An implementation of time matching in st_extract() for polygons is added to loreabad6/stars@time-polygon
  • Currently it does an internal aggregation for every passed geometry and then removes the redundant information.
  • The returned object is an sf, it would be good if, when passing a post_array to the at argument should return a post_array with the extra attribute.
(ext = st_extract(
  rdc,
  at = tab,
  time_column = "datetime",
  FUN = mean,
  na.rm = TRUE
))
#> Simple feature collection with 25 features and 2 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -0.2974337 ymin: -0.00297557 xmax: 0.9730806 ymax: 1.153558
#> Geodetic CRS:  WGS 84
#> First 10 features:
#>              z   datetime                       geometry
#> 1  -0.71838423 2020-10-01 POLYGON ((0.5474949 0.80889...
#> 2   0.09999119 2020-10-02 POLYGON ((0.4961102 0.87283...
#> 3   0.35655307 2020-10-03 POLYGON ((0.5578801 0.86163...
#> 4   0.04403025 2020-10-04 POLYGON ((0.5652241 0.87205...
#> 5  -0.17282679 2020-10-05 POLYGON ((0.6063791 0.83041...
#> 6  -0.07874849 2020-10-01 POLYGON ((0.2791708 0.83373...
#> 7  -0.03736406 2020-10-02 POLYGON ((0.3298312 0.76120...
#> 8  -0.21414059 2020-10-03 POLYGON ((0.3796448 0.76785...
#> 9   0.22340555 2020-10-04 POLYGON ((0.3642467 0.77972...
#> 10  0.19357414 2020-10-05 POLYGON ((0.2665368 0.86499...

arr |> mutate(z = ext$z)
#> stars object with 2 dimensions and 2 attributes
#> attribute(s):
#>          geometry         z           
#>  POLYGON      :25   Min.   :-0.71838  
#>  epsg:4326    : 0   1st Qu.:-0.21414  
#>  +proj=long...: 0   Median : 0.09999  
#>                     Mean   : 0.08041  
#>                     3rd Qu.: 0.35655  
#>                     Max.   : 0.73558  
#> dimension(s):
#>          from to     offset  delta refsys point
#> geom_sum    1  5         NA     NA WGS 84  TRUE
#> datetime    1  5 2020-10-01 1 days   Date FALSE
#>                                                            values
#> geom_sum POINT (0.647816 0.9018588),...,POINT (0.4690683 0.17772)
#> datetime                                                     NULL
Session info 
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.4.1 (2024-06-14)
#>  os       Ubuntu 22.04.4 LTS
#>  system   x86_64, linux-gnu
#>  ui       X11
#>  language (EN)
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       Etc/UTC
#>  date     2024-09-18
#>  pandoc   3.2 @ /usr/bin/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package     * version    date (UTC) lib source
#>  abind       * 1.4-8      2024-09-12 [1] RSPM (R 4.4.0)
#>  anytime       0.3.9      2020-08-27 [1] RSPM (R 4.4.0)
#>  class         7.3-22     2023-05-03 [2] CRAN (R 4.4.1)
#>  classInt      0.4-10     2023-09-05 [1] RSPM (R 4.4.0)
#>  cli           3.6.3      2024-06-21 [1] RSPM (R 4.4.0)
#>  colorspace    2.1-1      2024-07-26 [1] RSPM (R 4.4.0)
#>  cubble      * 1.0.0      2024-09-16 [1] Github (huizezhang-sherry/cubble@9563dfe)
#>  DBI           1.2.3      2024-06-02 [1] RSPM (R 4.4.0)
#>  digest        0.6.37     2024-08-19 [1] RSPM (R 4.4.0)
#>  dplyr       * 1.1.4      2023-11-17 [1] RSPM (R 4.4.0)
#>  e1071         1.7-16     2024-09-16 [1] RSPM (R 4.4.0)
#>  ellipsis      0.3.2      2021-04-29 [1] RSPM (R 4.4.0)
#>  evaluate      0.24.0     2024-06-10 [1] RSPM (R 4.4.0)
#>  fansi         1.0.6      2023-12-08 [1] RSPM (R 4.4.0)
#>  fastmap       1.2.0      2024-05-15 [1] RSPM (R 4.4.0)
#>  forcats     * 1.0.0      2023-01-29 [1] RSPM (R 4.4.0)
#>  fs            1.6.4      2024-04-25 [1] RSPM (R 4.4.0)
#>  generics      0.1.3      2022-07-05 [1] RSPM (R 4.4.0)
#>  ggplot2     * 3.5.1      2024-04-23 [1] RSPM (R 4.4.0)
#>  glue          1.7.0      2024-01-09 [1] RSPM (R 4.4.0)
#>  gtable        0.3.5      2024-04-22 [1] RSPM (R 4.4.0)
#>  hms           1.1.3      2023-03-21 [1] RSPM (R 4.4.0)
#>  htmltools     0.5.8.1    2024-04-04 [1] RSPM (R 4.4.0)
#>  KernSmooth    2.23-24    2024-05-17 [2] CRAN (R 4.4.1)
#>  knitr         1.48       2024-07-07 [1] RSPM (R 4.4.0)
#>  lifecycle     1.0.4      2023-11-07 [1] RSPM (R 4.4.0)
#>  lubridate   * 1.9.3      2023-09-27 [1] RSPM (R 4.4.0)
#>  magrittr      2.0.3      2022-03-30 [1] RSPM (R 4.4.0)
#>  munsell       0.5.1      2024-04-01 [1] RSPM (R 4.4.0)
#>  ncdf4         1.23       2024-08-17 [1] RSPM (R 4.4.0)
#>  pillar        1.9.0      2023-03-22 [1] RSPM (R 4.4.0)
#>  pkgconfig     2.0.3      2019-09-22 [1] RSPM (R 4.4.0)
#>  post        * 0.0.0.9000 2024-09-16 [1] local
#>  proxy         0.4-27     2022-06-09 [1] RSPM (R 4.4.0)
#>  purrr       * 1.0.2      2023-08-10 [1] RSPM (R 4.4.0)
#>  R6            2.5.1      2021-08-19 [1] RSPM (R 4.4.0)
#>  Rcpp          1.0.13     2024-07-17 [1] RSPM (R 4.4.0)
#>  readr       * 2.1.5      2024-01-10 [1] RSPM (R 4.4.0)
#>  reprex        2.1.0      2024-01-11 [1] RSPM (R 4.4.0)
#>  rlang         1.1.4      2024-06-04 [1] RSPM (R 4.4.0)
#>  rmarkdown     2.28       2024-08-17 [1] RSPM (R 4.4.0)
#>  rstudioapi    0.16.0     2024-03-24 [1] RSPM (R 4.4.0)
#>  scales        1.3.0      2023-11-28 [1] RSPM (R 4.4.0)
#>  sessioninfo   1.2.2      2021-12-06 [1] RSPM (R 4.4.0)
#>  sf          * 1.0-18     2024-09-18 [1] Github (r-spatial/sf@35f5f8b)
#>  stars       * 0.6-7      2024-09-18 [1] Github (loreabad6/stars@f70aca5)
#>  stringi       1.8.4      2024-05-06 [1] RSPM (R 4.4.0)
#>  stringr     * 1.5.1      2023-11-14 [1] RSPM (R 4.4.0)
#>  tibble      * 3.2.1      2023-03-20 [1] RSPM (R 4.4.0)
#>  tidyr       * 1.3.1      2024-01-24 [1] RSPM (R 4.4.0)
#>  tidyselect    1.2.1      2024-03-11 [1] RSPM (R 4.4.0)
#>  tidyverse   * 2.0.0      2023-02-22 [1] RSPM (R 4.4.0)
#>  timechange    0.3.0      2024-01-18 [1] RSPM (R 4.4.0)
#>  tsibble       1.1.5      2024-06-27 [1] RSPM (R 4.4.0)
#>  tzdb          0.4.0      2023-05-12 [1] RSPM (R 4.4.0)
#>  units         0.8-5      2023-11-28 [1] RSPM (R 4.4.0)
#>  utf8          1.2.4      2023-10-22 [1] RSPM (R 4.4.0)
#>  vctrs         0.6.5      2023-12-01 [1] RSPM (R 4.4.0)
#>  withr         3.0.1      2024-07-31 [1] RSPM (R 4.4.0)
#>  xfun          0.47       2024-08-17 [1] RSPM (R 4.4.0)
#>  yaml          2.3.10     2024-07-26 [1] RSPM (R 4.4.0)
#> 
#>  [1] /usr/local/lib/R/site-library
#>  [2] /usr/local/lib/R/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────
@loreabad6 loreabad6 changed the title Let aggregate.stars() accept post_arrays and include time matching {stars} functions for raster data cube and post object interaction, including time matching Sep 18, 2024
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