badge generation fix

.github/workflows/test-coverage-local.yaml

@@ -77,13 +77,21 @@ jobs:
           export COV=${{ steps.get-values.outputs.coverage }}
           COLOR=$(node -p '+process.env.COV >= 95 ? `green` : `orange`')
           mkdir -p badges
-          badgen -j coverage -s $COV% -c $COLOR > .github/badges/coverage.svg
+          badgen -j coverage -s $COV% -c $COLOR > ${{ github.workspace }}/badges/coverage.svg
   
-      - name: Deploy Badges
-        uses: stefanzweifel/git-auto-commit-action@v4
+      - name: Checkout artifacts branch
+        uses: actions/checkout@v4
         with:
-          commit_message: "Update badges [skip ci]"
-          branch: main
-          file_pattern: .github/badges/coverage.svg
-
+          ref: artifacts
+          persist-credentials: false
 
+      - name: Commit and Push Badge to Artifacts Branch
+        run: |
+          git config --global user.name "github-actions[bot]"
+          git config --global user.email "github-actions[bot]@users.noreply.github.com"
+          mkdir -p actions/workflows/test-coverage-local.yaml
+          mv badges/coverage.svg actions/workflows/test-coverage-local.yaml/coverage.svg
+          git add actions/workflows/test-coverage-local.yaml/coverage.svg
+          git commit -m "Update coverage badge [skip ci]"
+          git push origin artifacts
+        if: success()

README.Rmd

@@ -18,7 +18,7 @@ knitr::opts_chunk$set(
 
 <!-- badges: start -->
 <!-- [![cov](https://docs.ropensci.org/chopin/badges/coverage.svg)](https://github.com/ropensci/chopin/actions) -->
-![Coverage](https://raw.githubusercontent.com/ropensci/chopin/main/.github/badges/coverage.svg)
+![Coverage](https://github.com/ropensci/chopin/actions/workflows/test-coverage-local.yaml/coverage.svg)
 [![R-CMD-check](https://github.com/ropensci/chopin/actions/workflows/check-standard.yaml/badge.svg)](https://github.com/ropensci/chopin/actions/workflows/check-standard.yaml)
 [![Status at rOpenSci Software Peer Review](https://badges.ropensci.org/638_status.svg)](https://github.com/ropensci/software-review/issues/638)
 [![runiverse-package](https://ropensci.r-universe.dev/badges/chopin?scale=1&color=pink&style=round)](https://ropensci.r-universe.dev/chopin)

README.md

@@ -2,9 +2,10 @@
 # Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing <a href="https://docs.ropensci.org/chopin/"><img src="man/figures/logo.svg" align="right" height="210" alt="overlapping irregular grid polygons filled with orange, green, and teal" /></a>
 
 <!-- badges: start -->
+
 <!-- [![cov](https://docs.ropensci.org/chopin/badges/coverage.svg)](https://github.com/ropensci/chopin/actions) -->
 
-![Coverage](https://raw.githubusercontent.com/ropensci/chopin/main/.github/badges/coverage.svg)
+![Coverage](https://github.com/ropensci/chopin/actions/workflows/test-coverage-local.yaml/coverage.svg)
 [![R-CMD-check](https://github.com/ropensci/chopin/actions/workflows/check-standard.yaml/badge.svg)](https://github.com/ropensci/chopin/actions/workflows/check-standard.yaml)
 [![Status at rOpenSci Software Peer
 Review](https://badges.ropensci.org/638_status.svg)](https://github.com/ropensci/software-review/issues/638)
@@ -55,19 +56,21 @@ multiple raster file paths into `par_multirasters()`. **Finally,** users
 run `par_*()` function with the configurations set above to compute
 spatial variables from input data in parallel:
 
-- `par_grid`: parallelize over artificial grid polygons that are
-  generated from the maximum extent of inputs. `par_pad_grid` is used to
-  generate the grid polygons before running this function.
+  - `par_grid`: parallelize over artificial grid polygons that are
+    generated from the maximum extent of inputs. `par_pad_grid` is used
+    to generate the grid polygons before running this function.
+
+  - `par_hierarchy`: parallelize over hierarchy coded in identifier
+    fields (for example, census blocks in each county in the US)
 
-- `par_hierarchy`: parallelize over hierarchy coded in identifier fields
-  (for example, census blocks in each county in the US)
+  - `par_multirasters`: parallelize over multiple raster files
 
-- `par_multirasters`: parallelize over multiple raster files
+  - Each of the `par_*` functions introduced above has `mirai` version
+    with a suffix `_mirai` after the function names: `par_grid_mirai`,
+    `par_hierarchy_mirai`, and `par_multirasters`. These functions will
+    work properly after creating daemons with `mirai::daemons`.
 
-- Each of the `par_*` functions introduced above has `mirai` version
-  with a suffix `_mirai` after the function names: `par_grid_mirai`,
-  `par_hierarchy_mirai`, and `par_multirasters`. These functions will
-  work properly after creating daemons with `mirai::daemons`.
+<!-- end list -->
 
 ``` r
 mirai::daemons(4L, dispatcher = "process")
@@ -87,14 +90,14 @@ classes for spatial data. Raster-vector overlay is done with
 `exactextractr`. Three helper functions encapsulate multiple geospatial
 data calculation steps over multiple CPU threads.
 
-- `extract_at`: extract raster values with point buffers or polygons
-  with or without kernel weights
+  - `extract_at`: extract raster values with point buffers or polygons
+    with or without kernel weights
 
-- `summarize_sedc`: calculate sums of [exponentially decaying
-  contributions](https://mserre.sph.unc.edu/BMElab_web/SEDCtutorial/index.html)
+  - `summarize_sedc`: calculate sums of [exponentially decaying
+    contributions](https://mserre.sph.unc.edu/BMElab_web/SEDCtutorial/index.html)
 
-- `summarize_aw`: area-weighted covariates based on target and reference
-  polygons
+  - `summarize_aw`: area-weighted covariates based on target and
+    reference polygons
 
 ### Function selection guide
 
@@ -105,32 +108,33 @@ users with large vector data.
 
 In **raster-oriented selection**, we suggest four factors to consider:
 
-- Number of raster files: for multiple files, `par_multirasters` is
-  recommended. When there are multiple rasters that share the same
-  extent and resolution, consider stacking the rasters into multilayer
-  SpatRaster object by calling `terra::rast(filenames)`.
-- Raster resolution: We suggest 100 meters as a threshold. Rasters with
-  resolution coarser than 100 meters and a few layers would be better
-  for the direct call of `exactextractr::exact_extract()`.
-- Raster extent: Using `SpatRaster` in `exactextractr::exact_extract()`
-  is often minimally affected by the raster extent.
-- Memory size: `max_cells_in_memory` argument value of
-  `exactextractr::exact_extract()`, raster resolution, and the number of
-  layers in `SpatRaster` are multiplicatively related to the memory
-  usage.
+  - Number of raster files: for multiple files, `par_multirasters` is
+    recommended. When there are multiple rasters that share the same
+    extent and resolution, consider stacking the rasters into multilayer
+    SpatRaster object by calling `terra::rast(filenames)`.
+  - Raster resolution: We suggest 100 meters as a threshold. Rasters
+    with resolution coarser than 100 meters and a few layers would be
+    better for the direct call of `exactextractr::exact_extract()`.
+  - Raster extent: Using `SpatRaster` in
+    `exactextractr::exact_extract()` is often minimally affected by the
+    raster extent.
+  - Memory size: `max_cells_in_memory` argument value of
+    `exactextractr::exact_extract()`, raster resolution, and the number
+    of layers in `SpatRaster` are multiplicatively related to the memory
+    usage.
 
 ![](man/figures/README-flowchart-raster.png)
 
 For **vector-oriented selection**, we suggest three factors to consider:
 
-- Number of features: When the number of features is over 100,000,
-  consider using `par_grid` or `par_hierarchy` to split the data into
-  smaller chunks.
-- Hierarchical structure: If the data has a hierarchical structure,
-  consider using `par_hierarchy` to parallelize the operation.
-- Data grouping: If the data needs to be grouped in similar sizes,
-  consider using `par_pad_balanced` or `par_pad_grid` with
-  `mode = "grid_quantile"`.
+  - Number of features: When the number of features is over 100,000,
+    consider using `par_grid` or `par_hierarchy` to split the data into
+    smaller chunks.
+  - Hierarchical structure: If the data has a hierarchical structure,
+    consider using `par_hierarchy` to parallelize the operation.
+  - Data grouping: If the data needs to be grouped in similar sizes,
+    consider using `par_pad_balanced` or `par_pad_grid` with `mode =
+    "grid_quantile"`.
 
 ![](man/figures/README-flowchart-vector.png)
 
@@ -174,7 +178,7 @@ library(dplyr)
 #> 
 #>     intersect, setdiff, setequal, union
 library(sf)
-#> Linking to GEOS 3.12.2, GDAL 3.9.3, PROJ 9.4.1; sf_use_s2() is TRUE
+#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE
 library(terra)
 #> terra 1.7.83
 library(future)
@@ -261,7 +265,7 @@ system.time(
 )
 #> Input is a character. Attempt to read it with terra::rast...
 #>    user  system elapsed 
-#>   5.738   0.026   5.764
+#>   5.458   0.088   5.580
 ```
 
 #### Generate regular grid computational regions
@@ -348,7 +352,7 @@ system.time(
 #> Input is a character. Attempt to read it with terra::rast...
 #> ℹ Task at CGRIDID: 4 is successfully dispatched.
 #>    user  system elapsed 
-#>   0.243   0.019   6.303
+#>   0.336   0.045   7.788
 
 ncpoints_srtm <-
   extract_at(
@@ -406,7 +410,7 @@ system.time(
 )
 #> ℹ Input is not a character.
 #>    user  system elapsed 
-#>   0.063   0.001   6.246
+#>   0.083   0.000   8.004
 
 # remove mirai::daemons
 mirai::daemons(0L)
@@ -435,7 +439,7 @@ path_nchrchy <- file.path(wdir, "nc_hierarchy.gpkg")
 nc_data <- path_nchrchy
 nc_county <- sf::st_read(nc_data, layer = "county")
 #> Reading layer `county' from data source 
-#>   `/tmp/RtmpgQJVzZ/temp_libpath1ac4c210320fb/chopin/extdata/nc_hierarchy.gpkg' 
+#>   `/tmp/RtmpmJUPd2/temp_libpath3b3aa268543/chopin/extdata/nc_hierarchy.gpkg' 
 #>   using driver `GPKG'
 #> Simple feature collection with 100 features and 1 field
 #> Geometry type: POLYGON
@@ -444,7 +448,7 @@ nc_county <- sf::st_read(nc_data, layer = "county")
 #> Projected CRS: NAD83 / Conus Albers
 nc_tracts <- sf::st_read(nc_data, layer = "tracts")
 #> Reading layer `tracts' from data source 
-#>   `/tmp/RtmpgQJVzZ/temp_libpath1ac4c210320fb/chopin/extdata/nc_hierarchy.gpkg' 
+#>   `/tmp/RtmpmJUPd2/temp_libpath3b3aa268543/chopin/extdata/nc_hierarchy.gpkg' 
 #>   using driver `GPKG'
 #> Simple feature collection with 2672 features and 1 field
 #> Geometry type: MULTIPOLYGON
@@ -474,7 +478,7 @@ system.time(
 )
 #> Input is a character. Attempt to read it with terra::rast...
 #>    user  system elapsed 
-#>   0.712   0.000   0.712
+#>   0.621   0.010   0.613
 
 # hierarchical parallelization
 system.time(
@@ -592,7 +596,7 @@ system.time(
 #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37055 is dispatched.
 #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37047 is dispatched.
 #>    user  system elapsed 
-#>   0.262   0.064   5.886
+#>   0.419   0.033   7.799
 ```
 
 ### `par_multirasters()`: parallelize over multiple rasters
@@ -619,9 +623,9 @@ terra::writeRaster(ncelev, file.path(tdir, "test5.tif"), overwrite = TRUE)
 # check if the raster files were exported as expected
 testfiles <- list.files(tdir, pattern = "*.tif$", full.names = TRUE)
 testfiles
-#> [1] "/tmp/Rtmp4XLJrC/test1.tif" "/tmp/Rtmp4XLJrC/test2.tif"
-#> [3] "/tmp/Rtmp4XLJrC/test3.tif" "/tmp/Rtmp4XLJrC/test4.tif"
-#> [5] "/tmp/Rtmp4XLJrC/test5.tif"
+#> [1] "/tmp/RtmpiW9fAm/test1.tif" "/tmp/RtmpiW9fAm/test2.tif"
+#> [3] "/tmp/RtmpiW9fAm/test3.tif" "/tmp/RtmpiW9fAm/test4.tif"
+#> [5] "/tmp/RtmpiW9fAm/test5.tif"
 ```
 
 ``` r
@@ -638,32 +642,32 @@ system.time(
 )
 #> ℹ Input is not a character.
 #> Input is a character. Attempt to read it with terra::rast...
-#> ℹ Your input function at /tmp/Rtmp4XLJrC/test1.tif is dispatched.
+#> ℹ Your input function at /tmp/RtmpiW9fAm/test1.tif is dispatched.
 #> 
 #> Input is a character. Attempt to read it with terra::rast...
-#> ℹ Your input function at /tmp/Rtmp4XLJrC/test2.tif is dispatched.
+#> ℹ Your input function at /tmp/RtmpiW9fAm/test2.tif is dispatched.
 #> 
 #> Input is a character. Attempt to read it with terra::rast...
-#> ℹ Your input function at /tmp/Rtmp4XLJrC/test3.tif is dispatched.
+#> ℹ Your input function at /tmp/RtmpiW9fAm/test3.tif is dispatched.
 #> 
 #> Input is a character. Attempt to read it with terra::rast...
-#> ℹ Your input function at /tmp/Rtmp4XLJrC/test4.tif is dispatched.
+#> ℹ Your input function at /tmp/RtmpiW9fAm/test4.tif is dispatched.
 #> 
 #> Input is a character. Attempt to read it with terra::rast...
-#> ℹ Your input function at /tmp/Rtmp4XLJrC/test5.tif is dispatched.
+#> ℹ Your input function at /tmp/RtmpiW9fAm/test5.tif is dispatched.
 #>    user  system elapsed 
-#>   1.389   0.070   2.219
+#>   1.288   0.090   2.901
 knitr::kable(head(res))
 ```
 
-|      mean | base_raster               |
-|----------:|:--------------------------|
-| 136.80203 | /tmp/Rtmp4XLJrC/test1.tif |
-| 189.76170 | /tmp/Rtmp4XLJrC/test1.tif |
-| 231.16968 | /tmp/Rtmp4XLJrC/test1.tif |
-|  98.03845 | /tmp/Rtmp4XLJrC/test1.tif |
-|  41.23463 | /tmp/Rtmp4XLJrC/test1.tif |
-| 270.96933 | /tmp/Rtmp4XLJrC/test1.tif |
+|      mean | base\_raster              |
+| --------: | :------------------------ |
+| 136.80203 | /tmp/RtmpiW9fAm/test1.tif |
+| 189.76170 | /tmp/RtmpiW9fAm/test1.tif |
+| 231.16968 | /tmp/RtmpiW9fAm/test1.tif |
+|  98.03845 | /tmp/RtmpiW9fAm/test1.tif |
+|  41.23463 | /tmp/RtmpiW9fAm/test1.tif |
+| 270.96933 | /tmp/RtmpiW9fAm/test1.tif |
 
 ``` r
 
@@ -699,7 +703,7 @@ pnts <- sf::st_as_sf(pnts)
 pnts$pid <- sprintf("RPID-%04d", seq(1, 5000))
 rd1 <- sf::st_read(path_ncrd1)
 #> Reading layer `ncroads_first' from data source 
-#>   `/tmp/RtmpgQJVzZ/temp_libpath1ac4c210320fb/chopin/extdata/ncroads_first.gpkg' 
+#>   `/tmp/RtmpmJUPd2/temp_libpath3b3aa268543/chopin/extdata/ncroads_first.gpkg' 
 #>   using driver `GPKG'
 #> Simple feature collection with 620 features and 4 fields
 #> Geometry type: MULTILINESTRING
@@ -752,11 +756,11 @@ system.time(
   restr <- terra::nearest(x = terra::vect(pntst), y = terra::vect(rd1t))
 )
 #>    user  system elapsed 
-#>   0.461   0.000   0.462
+#>   0.396   0.000   0.397
 
 pnt_path <- file.path(tdir, "pntst.gpkg")
 sf::st_write(pntst, pnt_path)
-#> Writing layer `pntst' to data source `/tmp/Rtmp4XLJrC/pntst.gpkg' using driver `GPKG'
+#> Writing layer `pntst' to data source `/tmp/RtmpiW9fAm/pntst.gpkg' using driver `GPKG'
 #> Writing 5000 features with 1 fields and geometry type Point.
 
 # we use four threads that were configured above
@@ -802,11 +806,13 @@ system.time(
 #> ℹ Input is a character. Trying to read with terra .
 #> ℹ Task at CGRIDID: 8 is successfully dispatched.
 #>    user  system elapsed 
-#>   0.070   0.000   0.392
+#>   0.058   0.010   0.401
 ```
 
-- We will compare the results from the single-thread and multi-thread
-  calculation.
+  - We will compare the results from the single-thread and multi-thread
+    calculation.
+
+<!-- end list -->
 
 ``` r
 resj <- merge(restr, resd, by = c("from_x", "from_y"))