diff --git a/index.html b/index.html index fe83410..09daa6e 100644 --- a/index.html +++ b/index.html @@ -83,7 +83,9 @@

Install {rosettaPTF}remotes::install_github("ncss-tech/rosettaPTF")

Then load the rosetta-soil module by loading the R package. If you do not have an available python installation or rosetta-soil module you will be notified.

-library(rosettaPTF)
+library(rosettaPTF) +#> Error in system2(command = python, args = shQuote(script), stdout = TRUE, : +#> 'CreateProcess' failed to run 'C:\Users\ANDREW~1.BRO\ONEDRI~1\DOCUME~1\VIRTUA~1\R-RETI~1\Scripts\python.exe "C:/Users/Andrew.G.Brown/AppData/Local/R/win-library/4.3/reticulate/config/config.py"' "C:/Users/Andrew.G.Brown/OneDrive - USDA/Documents/.virtualenvs/r-reticulate/Scripts/python.exe"

rosetta-soil Python module @@ -136,14 +138,13 @@

Finding the python binaries 
 rosettaPTF::find_python()
-#> [1] "C:/Users/Andrew/OneDrive/Documents/.virtualenvs/r-reticulate/Scripts/python.exe"
+#> [1] "C:/Program Files/Python312/python.exe"

find_python() provides heuristics for setting up {reticulate} to use Python in commonly installed locations.

The first attempt makes use of Sys.which() to find installations available in the user path directory.

@@ -156,8 +157,7 @@

Install rosetta-soil Py

You can use install_rosetta() to install into custom environments by specifying envname as needed. After installing a new version of the module you should restart your R session.

 rosettaPTF::install_rosetta()
-#> Using virtual environment "C:/Users/Andrew/OneDrive/Documents/.virtualenvs/r-reticulate" ...
-#> + "C:/Users/Andrew/OneDrive/Documents/.virtualenvs/r-reticulate/Scripts/python.exe" -m pip install --upgrade --no-user rosetta-soil
+#> Using virtual environment "~/.virtualenvs/r-reticulate" ...
 #> [1] TRUE

Alternately, to install the module manually with pip you can run the following command. This assumes a Python 3 binary called python can be found on your path.

python -m pip install rosetta-soil
@@ -218,13 +218,16 @@

Soil Data Acce 
 library(soilDB)
 library(terra)
-#> terra 1.7.55
-library(rosettaPTF)
+#> Warning: package 'terra' was built under R version 4.3.3
+#> terra 1.7.78
+
+library(rosettaPTF)
 
 # obtain mukey map from SoilWeb Web Coverage Service (800m resolution SSURGO derived)
 res <- mukey.wcs(aoi = list(aoi = c(-114.16, 47.65,-114.08, 47.68), crs = 'EPSG:4326'))
-#> Loading required namespace: sf
-
+#> Loading required namespace: sf
+
+
 # request input data from SDA
 varnames <- c("sandtotal_r", "silttotal_r", "claytotal_r", "dbthirdbar_r")
 resprop <- get_SDA_property(property = varnames,
@@ -237,8 +240,9 @@ 
Soil Data Acce
 # run Rosetta on the mapunit-level aggregate data
 system.time(resrose <- run_rosetta(soildata[,varnames]))
 #>    user  system elapsed 
-#>    0.19    0.03    0.22
-
+#>    0.03    0.00    0.06
+
+
 # transfer mukey to result
 resprop$mukey <- as.numeric(resprop$mukey)
 resrose$mukey <- as.numeric(soildata$mukey)
@@ -260,7 +264,7 @@ 

 

 
The above example shows how to create raster output based on discrete (SSURGO polygon derived) data. A more general case is when each raster cell has “unique” values (i.e. continuous raster inputs). run_rosetta() has an S3 method defined for SpatRaster input.

 
We previously merged the input data from SDA (an ordinary data.frame) into the RAT of res; exploiting the linkage between mukey and raster cells to make the map. For comparison with the mukey results above we stack de-ratified input layers and create a new SpatRaster.

-
+
 res3 <- rast(list(
   res2[["sandtotal_r"]],
   res2[["silttotal_r"]],
@@ -271,8 +275,9 @@ 

 # SpatRaster to data.frame interface (one call on all cells)
 system.time(test2 <- run_rosetta(res3))
 #>    user  system elapsed 
-#>   51.77    5.69   56.06
-
+#>    6.20    0.55   14.42

+
+
 # make a plot of the predicted Ksat (identical to mukey-based results)
 plot(test2, "log10_Ksat_mean")

 

@@ -286,7 +291,7 @@ 
Extended Output with Rosett
 
Make a Rosetta class instance for running extended output methods
 

 
Note that each instance of Rosetta has a fixed version and model code, so if you have heterogeneous input you need to iterate over model code.

-
+
 # defaults are version 3 and model code 3 (4 parameters: sand, silt, clay and bulk density)
 my_rosetta <- Rosetta(rosetta_version = 3, model_code = 3)

 

@@ -294,7 +299,7 @@ 
Make
 

 predict() Rosetta Parameter Values and Standard Deviations from a Rosetta instance
 

-
+
 predict(my_rosetta, list(c(30, 30, 40, 1.5), c(55, 25, 20, 1.1)))
 #> [[1]]
 #>            [,1]     [,2]      [,3]      [,4]      [,5]
@@ -310,7 +315,7 @@ 
Extended Rosetta Predictions, Parameter Distributions and Summary Statistics after Zhang & Schaap (2017) with ann_predict()
 
 

-
+
 ann_predict(my_rosetta, list(c(30, 30, 40, 1.5), c(55, 25, 20, 1.1)))
 #> ann_predict() is defined for objects with class Rosetta; see `Rosetta()` to create a new instance
 #> $var_names
@@ -450,7 +455,7 @@ 
Developers

 
Dev status

 
    
 
  • R-CMD-check
    • 
-
  • HTML Docs
    • 
+
  • HTML Docs
    • 
 
  • codecov
    • 
 

 

diff --git a/pkgdown.yml b/pkgdown.yml
index 038bad6..4f1f317 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -2,5 +2,5 @@ pandoc: 3.1.11
 pkgdown: 2.0.9
 pkgdown_sha: ~
 articles: {}
-last_built: 2024-05-28T18:47Z
+last_built: 2024-05-28T18:49Z
 
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