[pre-commit.ci] pre-commit autoupdate

.Rprofile

@@ -25,11 +25,8 @@ options(
   radian.tab_size = 2,
   radian.complete_while_typing = TRUE,
   scipen = 999,
-
   clustermq.template = "slurm_clustermq.tmpl",
-
   precommit.executable = "/home/patrick/.local/bin/pre-commit",
-
   renv.settings.auto.snapshot = TRUE
 )
 suppressMessages(require(drake))
@@ -49,4 +46,3 @@ if (interactive() && "tibble" %in% rownames(utils::installed.packages())) {
 #   knitr::purl(rmd, output = tempR)
 #   sys.source(tempR, envir = envir)
 # }
-

.pre-commit-config.yaml

@@ -2,7 +2,7 @@
 # R specific hooks: https://github.com/lorenzwalthert/precommit
 repos:
 -   repo: https://github.com/lorenzwalthert/precommit
-    rev: v0.0.0.9027
+    rev: v0.2.0
     hooks: 
     # any R project
     -   id: style-files
@@ -17,7 +17,7 @@ repos:
     # -   id: deps-in-desc
     # -   id: codemeta-description-updated
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v2.4.0
+    rev: v4.0.1
     hooks: 
     -   id: check-added-large-files
         args: ['--maxkb=200']

R/04-fun-sentinel-processing.R

@@ -20,7 +20,8 @@ get_records <- function(aoi, date, processing_level) {
   login_CopHub("be-marc", password = "ISQiwQDl")
 
   # Get records from Sentinel-2 in time range
-  records <- getSpatialData::get_records(time_range = date,
+  records <- getSpatialData::get_records(
+    time_range = date,
     products = "Sentinel-2", as_sf = FALSE
   )
 

_drake.R

@@ -35,21 +35,17 @@ R.utils::sourceDirectory("code")
 plan_paper <- bind_plans(
   download_data_plan,
   hyperspectral_processing_plan,
-
   data_preprocessing_plan,
   tasks_plan,
-
   filter_eda_plan,
   param_sets_plan,
   learners_plan,
   filter_wrapper_plan,
   resampling_plan,
   tune_ctrl_plan,
   tune_wrapper_plan,
-
   benchmark_plan,
   bm_aggregated_plan,
-
   feature_imp_plan,
   reports_plan_paper
 )
@@ -58,11 +54,11 @@ plan_paper <- bind_plans(
 
 # config for long running tasks
 drake_config(plan_paper,
-   targets = c(
-     # replace this with the target name that should be computed
+  targets = c(
+    # replace this with the target name that should be computed
     #  c("eda_wfr")
     "benchmark_no_models"
-   ),
+  ),
   verbose = 1,
   lazy_load = "eager",
   packages = NULL,

analysis/eda.Rmd

@@ -28,10 +28,8 @@ R.utils::sourceDirectory("code")
 loadd(
   vi_data, nri_data,
   bands_data, coords_vi_nri_clean,
-
   filter_values,
   filter_info_gain_nbins,
-
   trees_with_bands,
   trees_with_bands_no_buffer
 )

analysis/eval-performance.Rmd

@@ -491,17 +491,14 @@ df_tbl <- getBMRTuneResults(bmr_inspect_tune, as.df = TRUE) %>%
     learner.id == "XGBOOST MBO Borda" & iter == 2 ~ "Laukiz1",
     learner.id == "XGBOOST MBO Borda" & iter == 3 ~ "Oiartzun",
     learner.id == "XGBOOST MBO Borda" & iter == 4 ~ "Laukiz2",
-
     learner.id == "SVM MBO Car" & iter == 1 ~ "Laukiz1",
     learner.id == "SVM MBO Car" & iter == 2 ~ "Oiartzun",
     learner.id == "SVM MBO Car" & iter == 3 ~ "Luiando",
     learner.id == "SVM MBO Car" & iter == 4 ~ "Laukiz2",
-
     learner.id == "RF MBO Car" & iter == 1 ~ "Luiando",
     learner.id == "RF MBO Car" & iter == 2 ~ "Laukiz2",
     learner.id == "RF MBO Car" & iter == 3 ~ "Laukiz1",
     learner.id == "RF MBO Car" & iter == 4 ~ "Oiartzun",
-
     TRUE ~ as.character(iter)
   )) %>%
   dplyr::rename(Learner = learner.id) %>%

analysis/feature-importance.Rmd

@@ -37,12 +37,10 @@ loadd(
   # Slurm resources suggestion: at least 20 cores and 2 GB / core = 40 GB pro job
   fi_permut_hr,
   fi_permut_vi,
-
   fi_ale_hr,
   fi_ale_vi,
   fi_ale_hr_gs20,
   fi_ale_vi_gs20,
-
   df_wavelengths_from_indices,
   spec_sigs
 )
@@ -66,7 +64,7 @@ fi_ranked_hr
 
 fi_ranked_vi <- fi_permut_vi$res %>%
   tibble::rownames_to_column("measure") %>%
-  dplyr::select(-contains("ID..")) %>% 
+  dplyr::select(-contains("ID..")) %>%
   tidyr::pivot_longer(
     cols = !measure,
     values_to = "importance", names_to = "feature"
@@ -203,7 +201,6 @@ plot_reflectance_imp_hr <- function(data) {
       size = 3,
       na.rm = TRUE
     ) +
-
     scale_color_manual(values = c(
       "HR: Rank 11:122" = "grey",
       "HR: Rank 1:10" = "black",
@@ -317,7 +314,6 @@ plot_reflectance_imp_vi <- function(data) {
       aes(x = wavelength, y = importance, group = class, color = "VI: Rank 11:89"),
       size = 0.2, linetype = "dashed"
     ) +
-
     scale_color_manual(values = c(
       "VI: Rank 11:89" = "grey",
       "VI: Rank 1:10" = "black",
@@ -471,7 +467,7 @@ ALE plots via package {iml}
 Top ten HR features from permutation Vimp
 
 ```{r fi-hr-ale, dev = c("png", "pdf"), warning=FALSE}
-ale_p1 = fi_ale_hr$plot(
+ale_p1 <- fi_ale_hr$plot(
   features = df_wavelengths_from_hr$class,
   ncol = 2
 ) & scale_y_continuous(
@@ -487,27 +483,27 @@ ale_p1 = fi_ale_hr$plot(
   )
 
 # Custom y axis breaks for subplots
-ale_p1[[2]] = ale_p1[[2]] + scale_y_continuous(
+ale_p1[[2]] <- ale_p1[[2]] + scale_y_continuous(
   n.breaks = 3
 )
 
-ale_p1[[3]] = ale_p1[[3]] + scale_y_continuous(
+ale_p1[[3]] <- ale_p1[[3]] + scale_y_continuous(
   n.breaks = 4
 )
 
-ale_p1[[7]] = ale_p1[[7]] + scale_y_continuous(
+ale_p1[[7]] <- ale_p1[[7]] + scale_y_continuous(
   n.breaks = 4
 )
 
-ale_p1[[8]] = ale_p1[[8]] + scale_y_continuous(
+ale_p1[[8]] <- ale_p1[[8]] + scale_y_continuous(
   n.breaks = 4
 )
 
-ale_p1[[9]] = ale_p1[[9]] + scale_y_continuous(
+ale_p1[[9]] <- ale_p1[[9]] + scale_y_continuous(
   n.breaks = 4
 )
 
-ale_p1[[10]] = ale_p1[[10]] + scale_y_continuous(
+ale_p1[[10]] <- ale_p1[[10]] + scale_y_continuous(
   n.breaks = 4
 )
 ale_p1
@@ -518,7 +514,7 @@ ale_p1
 Top ten HR features from permutation Vimp
 
 ```{r, warning=FALSE, message=FALSE}
-ale_p2 = fi_ale_hr_gs20$plot(
+ale_p2 <- fi_ale_hr_gs20$plot(
   features = df_wavelengths_from_hr$class,
   ncol = 2
 ) & scale_y_continuous(
@@ -534,25 +530,25 @@ ale_p2 = fi_ale_hr_gs20$plot(
   )
 
 # Custom y axis breaks for subplots
-ale_p2[[1]] = ale_p2[[1]] + scale_y_continuous(
+ale_p2[[1]] <- ale_p2[[1]] + scale_y_continuous(
   n.breaks = 4
 )
-ale_p2[[2]] = ale_p2[[2]] + scale_y_continuous(
+ale_p2[[2]] <- ale_p2[[2]] + scale_y_continuous(
   n.breaks = 5
 )
-ale_p2[[3]] = ale_p2[[3]] + scale_y_continuous(
+ale_p2[[3]] <- ale_p2[[3]] + scale_y_continuous(
   n.breaks = 4
 )
-ale_p2[[4]] = ale_p2[[4]] + scale_y_continuous(
+ale_p2[[4]] <- ale_p2[[4]] + scale_y_continuous(
   n.breaks = 4
 )
-ale_p2[[6]] = ale_p2[[6]] + scale_y_continuous(
+ale_p2[[6]] <- ale_p2[[6]] + scale_y_continuous(
   n.breaks = 4
 )
-ale_p2[[7]] = ale_p2[[7]] + scale_y_continuous(
+ale_p2[[7]] <- ale_p2[[7]] + scale_y_continuous(
   n.breaks = 4
 )
-ale_p2[[10]] = ale_p2[[10]] + scale_y_continuous(
+ale_p2[[10]] <- ale_p2[[10]] + scale_y_continuous(
   n.breaks = 3
 )
 ale_p2
@@ -565,7 +561,7 @@ ale_p2
 Top ten VI features from permutation Vimp
 
 ```{r fi-vi-ale, dev = c("png", "pdf"), warning=FALSE}
-ale_p3 = fi_ale_vi$plot(
+ale_p3 <- fi_ale_vi$plot(
   features = df_wavelengths_from_indices_label$class,
   ncol = 2
 ) & scale_y_continuous(
@@ -581,16 +577,16 @@ ale_p3 = fi_ale_vi$plot(
   )
 
 # Custom y axis breaks for subplots
-ale_p3[[1]] = ale_p3[[1]] + scale_y_continuous(
+ale_p3[[1]] <- ale_p3[[1]] + scale_y_continuous(
   n.breaks = 4
 )
-ale_p3[[2]] = ale_p3[[2]] + scale_y_continuous(
+ale_p3[[2]] <- ale_p3[[2]] + scale_y_continuous(
   n.breaks = 4
 )
-ale_p3[[3]] = ale_p3[[3]] + scale_y_continuous(
+ale_p3[[3]] <- ale_p3[[3]] + scale_y_continuous(
   n.breaks = 4
 )
-ale_p3[[4]] = ale_p3[[4]] + scale_y_continuous(
+ale_p3[[4]] <- ale_p3[[4]] + scale_y_continuous(
   n.breaks = 4
 )
 ale_p3
@@ -601,7 +597,7 @@ ale_p3
 Top ten HR features from permutation Vimp
 
 ```{r, warning=FALSE}
-ale_p4 = fi_ale_vi_gs20$plot(
+ale_p4 <- fi_ale_vi_gs20$plot(
   features = df_wavelengths_from_indices_label$class,
   ncol = 2
 ) & scale_y_continuous(
@@ -617,13 +613,13 @@ ale_p4 = fi_ale_vi_gs20$plot(
   )
 
 # Custom y axis breaks for subplots
-ale_p4[[2]] = ale_p3[[2]] + scale_y_continuous(
+ale_p4[[2]] <- ale_p3[[2]] + scale_y_continuous(
   n.breaks = 4
 )
-ale_p4[[3]] = ale_p3[[3]] + scale_y_continuous(
+ale_p4[[3]] <- ale_p3[[3]] + scale_y_continuous(
   n.breaks = 4
 )
-ale_p4[[4]] = ale_p3[[4]] + scale_y_continuous(
+ale_p4[[4]] <- ale_p3[[4]] + scale_y_continuous(
   n.breaks = 4
 )
 ale_p4

analysis/filter-correlation.Rmd

@@ -32,7 +32,6 @@ R.utils::sourceDirectory("R")
 drake::loadd(
   filter_values,
   filter_values_fselectorrcpp,
-
   filter_info_gain_nbins
 )
 ```

analysis/license.Rmd

@@ -10,7 +10,7 @@ editor_options:
 # MIT License
 
 ```{r echo = FALSE, results='asis'}
-year = lubridate::year(lubridate::today())
+year <- lubridate::year(lubridate::today())
 cat(sprintf("Copyright (c) 2019-%s Patrick Schratz", year))
 ```
 

analysis/report-defoliation.Rmd

@@ -26,7 +26,6 @@ loadd(
   defoliation_raster_relative,
   defoliation_df,
   defoliation_df_relative,
-
   tune_xgboost_plan_project
 )
 ```
@@ -179,6 +178,6 @@ suppressMessages(print(hist))
 ```
 
 ```{r echo = FALSE}
-#system("rsync -rlptDivzog --chown=*:www-data --chmod=g+r,o+r /home/patrick/papers/2019-feature-selection/docs/figure/report-defoliation.Rmd/defol* [email protected]:/home/www/life-healthy-forest/action-B1-spatial-mapping/defoliation-maps/")
+# system("rsync -rlptDivzog --chown=*:www-data --chmod=g+r,o+r /home/patrick/papers/2019-feature-selection/docs/figure/report-defoliation.Rmd/defol* [email protected]:/home/www/life-healthy-forest/action-B1-spatial-mapping/defoliation-maps/")
 ```
 

analysis/spectral-signatures.Rmd

@@ -69,8 +69,10 @@ wavelength <- c(
 ```
 
 ```{r spectral-signatures-3 }
-speclibs <- purrr::map(data_hs_preprocessed_paper,
-  ~ hsdar::speclib(.x, wavelength))
+speclibs <- purrr::map(
+  data_hs_preprocessed_paper,
+  ~ hsdar::speclib(.x, wavelength)
+)
 ```
 
 ```{r spectral-signatures, dev=c("png", "pdf"), warning=FALSE}

analysis/test/cloud-cover.Rmd

@@ -5,9 +5,11 @@ author: "Marc Becker"
 ---
 
 ```{r setup, include=FALSE}
-knitr::opts_knit$set(base.dir = 'analysis/rmd/')
-knitr::opts_chunk$set(fig.retina = 3, fig.align = 'center', fig.width = 6.93, 
-                      fig.height = 6.13, out.width = "100%")
+knitr::opts_knit$set(base.dir = "analysis/rmd/")
+knitr::opts_chunk$set(
+  fig.retina = 3, fig.align = "center", fig.width = 6.93,
+  fig.height = 6.13, out.width = "100%"
+)
 source(here::here("code/99-packages.R"))
 
 loadd(records, aoi, cloud_mosaic)
@@ -17,40 +19,40 @@ loadd(records, aoi, cloud_mosaic)
 records %<>% split(records[[2]]$beginposition)
 
 # Unique dates
-date_scenes =  
+date_scenes <-
   records %>%
   map(~ dplyr::select(., beginposition)) %>%
   map(~ unique(.)) %>%
   map_chr(~ str_sub(., 1, 10))
 
 # Scenes per mosaic/ date
-count_scenes =
+count_scenes <-
   records %>%
   map_dbl(~ nrow(.))
 
 # Row number of mosaic
-row_scenes =
+row_scenes <-
   records %>%
   map(~ dplyr::select(., relativeorbitnumber)) %>%
   map(~ pull(.)) %>%
   map_chr(~ unique(.))
 
 # Calculate area of aoi
-aoi_area = st_area(aoi) %>%
+aoi_area <- st_area(aoi) %>%
   sum()
 
 # Calculate cloud cover
-cloud_scenes =
+cloud_scenes <-
   cloud_mosaic %>%
-  map(~ st_read(., quiet=TRUE)) %>%
+  map(~ st_read(., quiet = TRUE)) %>%
   map(~ st_set_crs(., 25830)) %>%
   map(~ st_intersection(st_buffer(., 0), aoi)) %>%
   map(possibly(~ st_union(.), NA)) %>%
   map(possibly(~ st_area(.), NA)) %>%
   map_dbl(possibly(~ sum(.), NA)) %>%
-  map_dbl(possibly(~ ./aoi_area, NA))
+  map_dbl(possibly(~ . / aoi_area, NA))
 
-ext_scenes =
+ext_scenes <-
   records %>%
   map(~ dplyr::select(., footprint)) %>%
   map_depth(2, ~ st_as_sfc(.)) %>%