diff --git a/_pkgdown.yml b/_pkgdown.yml new file mode 100644 index 0000000..edb28bf --- /dev/null +++ b/_pkgdown.yml @@ -0,0 +1,4 @@ +url: https://joboog.github.io/r2ogs6 +template: + bootstrap: 5 + diff --git a/docs/404.html b/docs/404.html new file mode 100644 index 0000000..7d23f1d --- /dev/null +++ b/docs/404.html @@ -0,0 +1,86 @@ + + +
+ + + + +We are happy about all contributions!
+Open a new issue with your idea.
+Version 3, 29 June 2007
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+If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.
+To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found.
+`r2ogs6` is an R-API to the open-source multiphysics simulator [OpenGeoSys 6](https://www.opengeosys.org).
+Copyright (C) 2021 Helmholtz Centre for Environmental Research - UFZ
+
+: you can redistribute it and/or modify
+ This program is free software
+ it under the terms of the GNU General Public License as published by3 of the License, or
+ the Free Software Foundation, either version
+ (at your option) any later version.
+in the hope that it will be useful,
+ This program is distributed
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See thefor more details.
+ GNU General Public License
+
+ You should have received a copy of the GNU General Public License<http://www.gnu.org/licenses/>. along with this program. If not, see
Also add information on how to contact you by electronic and paper mail.
+If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:
+Copyright (C) 2021 Helmholtz Centre for Environmental Research - UFZ
+ r2ogs6 for details type 'show w'.
+ This program comes with ABSOLUTELY NO WARRANTY;
+ This is free software, and you are welcome to redistribute it'show c' for details. under certain conditions; type
The hypothetical commands show w
and show c
should show the appropriate parts of the General Public License. Of course, your program’s commands might be different; for a GUI interface, you would use an “about box”.
You should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see <http://www.gnu.org/licenses/>.
+The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read <http://www.gnu.org/philosophy/why-not-lgpl.html>.
+dev_workflow_vignette.Rmd
+library(r2ogs6)
Welcome to my dev guide on r2ogs6
. This is a collection of tips, useful info (and admittedly a few warnings) which will hopefully make your life a bit easier when developing this package.
Before we dive into any implementation details, we will take a look at how exactly this package is structured first. r2ogs6
was developed using the workflow described here. I strongly recommend keeping it that way as it will save you time and headaches.
…
+In the main folder R/
you will find a lot of scripts, most of which can be grouped into the following categories:
export_*.R
export functions
generate_*.R
code generation
read_in_*.R
import functions
ogs6_*.R
simulation class definitions
prj_*.R
class definitions for XML tags found in a .prj
file
*_utils.R
utility functions used in multiple scripts
r2ogs6
is largely built on top of S3 classes at the moment. For reasons I will elaborate on later, it is very viable to switch to R6 classes. But let’s look at what we have first.
….
+If you’ve familiarized yourself with OpenGeoSys 6, you know that there are a lot, and by a lot I mean a LOT of parameters and special cases regarding the .prj
XML tags. For a nice new class based on such a tag, you will have to consider all of them.
To save me (and you) a bit of typing, I’ve written a few useful functions for this.
+The first and arguably most important one is analyse_xml()
. It matches files in a folder, reads them in as XML and searches for XML elements of a given name. It then analyses those elements and returns useful information about them, namely the names of their attributes and child elements. It prints a summary of its findings and also returns a list which we will look at in a moment.
I used this function for two things: Analysing … . Secondly, as soon as I had decided which tags should be represented by a class, I used the function output for class generation.
+So say we have some .prj
files stored in a folder. I will show the workflow on a small dataset (that is, on a folder with only two .prj
files) here, the path I usually passed to analyse_xml()
was the directory containing all of the benchmark files for OpenGeoSys 6 which can be downloaded from here.
+test_folder <- system.file("extdata/vignettes_data/analyse_xml_demo",
+ package = "r2ogs6")
Now say we have decided we are going to make a class based on the element with tag name nonlinear_solver
. For readability reasons, I will store the results of analyse_xml()
in a variable and pass it to our generator function. If you want, you can skip this step and call analyse_xml()
in the generator function directly.
+analysis_results <- analyse_xml(path = test_folder,
+ pattern = "\\.prj$",
+ xpath = "//nonlinear_solver",
+ print_findings = TRUE)
+#>
+#> I parsed 2 valid XML files matching your pattern.
+#>
+#> I found at least one element named nonlinear_solver in the following file(s):
+#> beam.prj
+#> beam3d.prj
+#>
+#> In total, I found 5 element(s) named nonlinear_solver.
+#>
+#> These are the child elements I found:
+#> name ex_occ p_occ total total_mean
+#> 1 name 2 0.4 2 0.4
+#> 2 type 2 0.4 2 0.4
+#> 3 max_iter 2 0.4 2 0.4
+#> 4 linear_solver 2 0.4 2 0.4
+#> 5 maximum_iterations 1 0.2 1 0.2
+#> 6 error_tolerance 1 0.2 1 0.2
+#> 7 damping 1 0.2 1 0.2
First, I define my path and specify that only files with the ending .prj
will be parsed. I’m looking for elements named nonlinear_solver
, and I’m looking for them in the whole document. This often isn’t the best option since sometimes nodes may have the same name but contain different things depending on their exact position in the document, which is also the case here. To narrow it down further, change xpath
accordingly.
+analysis_results <- analyse_xml(path = test_folder,
+ pattern = "\\.prj$",
+ xpath = "/OpenGeoSysProject/nonlinear_solvers/nonlinear_solver",
+ print_findings = TRUE)
+#>
+#> I parsed 2 valid XML files matching your pattern.
+#>
+#> I found at least one element named nonlinear_solver in the following file(s):
+#> beam.prj
+#> beam3d.prj
+#>
+#> In total, I found 2 element(s) named nonlinear_solver.
+#>
+#> These are the child elements I found:
+#> name ex_occ p_occ total total_mean
+#> 1 name 2 1.0 2 1.0
+#> 2 type 2 1.0 2 1.0
+#> 3 max_iter 2 1.0 2 1.0
+#> 4 linear_solver 2 1.0 2 1.0
+#> 5 damping 1 0.5 1 0.5
Now we can be sure our future class will be generated from the correct parameters. analyse_xml()
returns a named list invisibly, let’s have a short look at it.
+analysis_results
+#> $xpath
+#> [1] "/OpenGeoSysProject/nonlinear_solvers/nonlinear_solver"
+#>
+#> $children
+#> name type max_iter linear_solver damping
+#> TRUE TRUE TRUE TRUE FALSE
+#>
+#> $attributes
+#> logical(0)
+#>
+#> $both_sorted
+#> name type max_iter linear_solver damping
+#> TRUE TRUE TRUE TRUE FALSE
You can see the list contains the xpath
parameter passed to analyse_xml()
, along with three named logical vectors called children
, attributes
and both_sorted
respectively. They can be read like this: If an attribute or a child of the element specified by xpath
always occurred, it is a required parameter for the new class. Else, it is an optional parameter. The logical vectors are sorted by occurrency, so the rarest children and attributes will go to the very end of their logical vector. Now, let’s generate some code!
For S3 classes, we generate a constructor like this:
+
+generate_constructor(params = analysis_results,
+ print_result = TRUE)
+#> new_prj_nonlinear_solver <- function(name,
+#> type,
+#> max_iter,
+#> linear_solver,
+#> damping = NULL) {
+#> structure(list(name = name,
+#> type = type,
+#> max_iter = max_iter,
+#> linear_solver = linear_solver,
+#> damping = damping,
+#> xpath = "nonlinear_solvers/nonlinear_solver",
+#> attr_names = c(),
+#> flatten_on_exp = character()
+#> ),
+#> class = "prj_nonlinear_solver"
+#> )
+#> }
+#>
For S3 classes, we generate a helper like this:
+
+generate_helper(params = analysis_results,
+ print_result = TRUE)
+#> #'prj_nonlinear_solver
+#> #'@description tag: nonlinear_solver
+#> #'@param name
+#> #'@param type
+#> #'@param max_iter
+#> #'@param linear_solver
+#> #'@param damping Optional:
+#> #'@export
+#> prj_nonlinear_solver <- function(name,
+#> type,
+#> max_iter,
+#> linear_solver,
+#> damping = NULL) {
+#>
+#> # Add coercing utility here
+#>
+#> new_prj_nonlinear_solver(name,
+#> type,
+#> max_iter,
+#> linear_solver,
+#> damping)
+#> }
+#>
For R6 classes, we generate a constructor like this:
+
+generate_R6(params = analysis_results,
+ print_result = TRUE)
+#> OGS6_nonlinear_solver <- R6::R6Class("OGS6_nonlinear_solver",
+#> public = list(
+#> #'@description
+#> #'Creates new OGS6_nonlinear_solverobject
+#> #'@param name
+#> #'@param type
+#> #'@param max_iter
+#> #'@param linear_solver
+#> #'@param damping Optional: initialize = function(name,
+#> type,
+#> max_iter,
+#> linear_solver,
+#> damping = NULL){
+#> self$name <- name
+#> self$type <- type
+#> self$max_iter <- max_iter
+#> self$linear_solver <- linear_solver
+#> self$damping <- damping
+#> }
+#> ),
+#>
+#> active = list(
+#> #'@field name
+#> #'Access to private parameter '.name'
+#> name = function(value) {
+#> if(missing(value)) {
+#> private$.name
+#> }else{
+#> private$.name <- value
+#> }
+#> },
+#>
+#> #'@field type
+#> #'Access to private parameter '.type'
+#> type = function(value) {
+#> if(missing(value)) {
+#> private$.type
+#> }else{
+#> private$.type <- value
+#> }
+#> },
+#>
+#> #'@field max_iter
+#> #'Access to private parameter '.max_iter'
+#> max_iter = function(value) {
+#> if(missing(value)) {
+#> private$.max_iter
+#> }else{
+#> private$.max_iter <- value
+#> }
+#> },
+#>
+#> #'@field linear_solver
+#> #'Access to private parameter '.linear_solver'
+#> linear_solver = function(value) {
+#> if(missing(value)) {
+#> private$.linear_solver
+#> }else{
+#> private$.linear_solver <- value
+#> }
+#> },
+#>
+#> #'@field damping
+#> #'Access to private parameter '.damping'
+#> damping = function(value) {
+#> if(missing(value)) {
+#> private$.damping
+#> }else{
+#> private$.damping <- value
+#> }
+#> },
+#>
+#> #'@field is_subclass
+#> #'Access to private parameter '.is_subclass'
+#> is_subclass = function() {
+#> private$.is_subclass
+#> },
+#>
+#> #'@field subclasses_names
+#> #'Access to private parameter '.subclasses_names'
+#> subclasses_names = function() {
+#> private$.subclasses_names
+#> },
+#>
+#> #'@field attr_names
+#> #'Access to private parameter '.attr_names'
+#> attr_names = function() {
+#> private$.attr_names
+#> }
+#> ),
+#>
+#> private = list(
+#> .name = NULL,
+#> .type = NULL,
+#> .max_iter = NULL,
+#> .linear_solver = NULL,
+#> .damping = NULL,
+#> .is_subclass = TRUE,
+#> .subclasses_names = character(),
+#> .attr_names = c(),
+#> )
+#> )
Ta-daa, you now have some nice stubs. Copy them into a script in the R
folder of this package, add some documentation and validation to it and you’re almost done.
Now that we have a class, we need to tell the package it exists. This is so when we’re reading in or exporting a .prj
file, it knows to automatically turn the content of our nonlinear_solver
tag into an object of our new class and the other way around. To achieve this, execute the code in data_raw/xpaths_for_classes.R
. What this will do is update the xpaths_for_classes
parameter, adding an entry for your class. Afterwards, run xpaths_for_classes[["your_class_name"]]
. It should return the xpath
parameter of your class like so:
+xpaths_for_classes[["prj_process"]]
+
+# A class can have multiple xpaths if the represented node occurs at different positions.
+xpaths_for_classes[["prj_convergence_criterion"]]
If the class you’ve created is a .prj
top level class or a child of a top level wrapper node like processes
, add a corresponding OGS6
private parameter and an active field. For example, the processes
node is represented as a list, so I added the private parameter .processes = list()
and the active field processes
.
A lot of things in the r2ogs6
package work in a way that is a bit “meta”. Often times, functions are called via eval(parse(text = call_string))
where call_string
has for example been concatenated out of info about the parameter names of a certain class. This saves a lot of code regarding import, export and script generation but requires that you’ve made the respective info available as shown here.
So we’ve analysed some files, generated some code, created a new class and registered it with the package… what now? That’s it actually, that’s the workflow. Well, at least it’s supposed to be.
+If that wasn’t it, I’m afraid you might have to take a look at the functions handling import, export and benchmark script generation. These are a bit tricky because they use recursion which so far has proven to be efficient structure-wise but not exactly fun to think about.
+ + + +ensemble_workflow_vignette.Rmd
Hi there! This is a practical guide on the OGS6_Ensemble
class from the r2ogs6
package. I will show you how you can use this class to set up ensemble runs for OpenGeoSys 6, extract the results and plot them.
If you want to follow this tutorial, you’ll need to have r2ogs6
installed and loaded. The prerequisites for r2ogs6
are described in detail in the r2ogs6 User Guide
vignette.
Additionally, you’ll need the following benchmark files:
+Theis’ Problem as described here
Theis solution for well pumping as described here
General instructions on how to download OpenGeoSys 6 benchmark files can be found here.
+We will consider the following parameters for our sensitivity analysis:
+permeability
porosity
storage
First, we create a simulation object to base our ensemble on and read in the .prj
file.
+# Change this to fit your system
+testdir_path <- tempdir()
+sim_path <- paste0(testdir_path, "/axisym_theis_sim")
+
+ogs6_obj <- OGS6$new(sim_name = "axisym_theis",
+ sim_path = sim_path)
+
+# Change this to fit your system
+prj_path <- system.file("extdata/benchmarks/AxiSymTheis/",
+ "axisym_theis.prj", package = "r2ogs6")
+
+read_in_prj(ogs6_obj, prj_path, read_in_gml = T)
Let’s create a small ensemble where we only alter the value of storage
. Say we don’t want to hardcode the values, but instead examine the effects of changing storage
by 1%, 10% and 50%. We can use the percentages_mode
parameter of OGS6_Ensemble
for this. It already defaults to TRUE
, below I’m merely being explicit for demonstration purposes.
+# Assign percentages
+percentages <- c(-50, -10, -1, 0, 1, 10, 50)
+
+# Define an ensemble object
+ogs6_ens <-
+ OGS6_Ensemble$new(
+ ogs6_obj = ogs6_obj,
+ parameters = list(list(ogs6_obj$media[[1]]$properties[[4]]$value,
+ percentages)),
+ percentages_mode = TRUE)
Now you can start the simulation.
+
+ogs6_ens$run_simulation()
+lapply(ogs6_ens$ensemble, ogs6_read_output_files)
Our simulations (hopefully) ran successfully - great! Now it’d be nice to see some results. Say we’re interested in the pressure
data.
+# This will get a combined dataframe
+storage_tbl <-
+ ogs6_ens$get_point_data(point_ids = c(0, 1, 2),
+ keys = c("pressure"))
You may leave out the point_ids
parameter. It defaults to all points in the dataset - I specify it here because there’s about 500 which would slow down building this vignette.
+# Let's look at the first 10 rows of the dataset
+head(storage_tbl, 10)
+#> # A tibble: 10 × 8
+#> id x y z pressure timestep sim_id perc
+#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
+#> 1 0 0.305 0 0 0 0 1 -50
+#> 2 0 0.305 0 0 6.99 8.64 1 -50
+#> 3 0 0.305 0 0 9.77 86.4 1 -50
+#> 4 0 0.305 0 0 13.3 1728 1 -50
+#> 5 0 0.305 0 0 16.2 24192 1 -50
+#> 6 0 0.305 0 0 16.6 172800 1 -50
+#> 7 0 0.305 0 0 16.6 604800 1 -50
+#> 8 0 0.305 0 0 16.6 864000 1 -50
+#> 9 1 305. 0 0 0 0 1 -50
+#> 10 1 305. 0 0 0 8.64 1 -50
You can see there’s one row per timestep for each point ID. Say we want to plot the average pressure for each timestep
, that is, the mean of the pressure
values where the rows have the same timestep
. We’ll consider only the first simulation for now.
+# Get average pressure from first simulation
+avg_pr_first_sim <- storage_tbl[storage_tbl$sim_id == 1,] %>%
+ group_by(timestep) %>%
+ summarize(avg_pressure = mean(pressure))
+
+# Plot pressure over time for first simulation
+ggplot(data = avg_pr_first_sim) +
+ geom_point(mapping = aes(x = as.numeric(row.names(avg_pr_first_sim)),
+ y = avg_pressure)) + xlab("Timestep")
+# Get average pressure for all simulations
+avg_pr_df <- storage_tbl %>%
+ group_by(sim_id, timestep) %>%
+ summarise(avg_pressure = mean(pressure))
+
+# Plot pressure over time for all simulations
+ggplot(avg_pr_df,
+ aes(x = as.numeric(as.factor(timestep)),
+ y = avg_pressure)) +
+ geom_point(aes(color = as.factor(sim_id))) +
+ geom_line(aes(color = as.factor(sim_id))) +
+ xlab("Timestep") +
+ labs(color = "sim id") +
+ facet_grid(rows = vars(sim_id))
Now we have the average pressure
over time for each of the simulations (rows). Since they’re pretty similar, let’s put them in the same plot for a better comparison.
+ggplot(avg_pr_df, aes(x = as.numeric(as.factor(timestep)),
+ y = avg_pressure,
+ group = sim_id)) +
+ geom_point(aes(color = as.factor(sim_id))) +
+ geom_line(aes(color = as.factor(sim_id))) +
+ labs(color = "sim id") +
+ xlab("Timestep")
So far we’ve only considered the storage
parameter. Now we want to have a look at how the other two parameters influence the simulation, so let’s put them into an ensemble together. For permeability
we can reuse the percentages
we already have. For porosity
, this doesn’t work - the original value is 1 and its values can only range between 0 and 1, so we’ll supply a shorter vector.
This time it’s important we set the OGS6_Ensemble
parameter sequential_mode
to TRUE
as this will change the supplied parameters sequentially which means in the end we have 18 (7 + 7 + 4) simulations which is equal to the sum of elements in the value vectors you supply (I’ve named them values
below for clarity, naming them is optional though).
The default FALSE
would give an error message because our value vectors do not have the same length and even if they had, it wouldn’t do what we want - the number of simulations would equal the length of one value vector (thus requiring them to be of the same length). Generally, set sequential_mode
to TRUE
if you want to examine the influence of parameters on a simulation independently. If you want to examine how the parameters influence each other as in wanting to test parameter combinations, the default mode is the way to go.
+# Change this to fit your system
+sim_path <- paste0(testdir_path, "/axisym_theis_sim_big")
+
+ogs6_obj$sim_path <- sim_path
+
+ogs6_ens_big <-
+ OGS6_Ensemble$new(
+ ogs6_obj = ogs6_obj,
+ parameters = list(per = list(ogs6_obj$media[[1]]$properties[[1]]$value,
+ values = percentages),
+ por = list(ogs6_obj$media[[1]]$properties[[3]]$value,
+ c(-50, -10, -1, 0)),
+ sto = list(ogs6_obj$media[[1]]$properties[[4]]$value,
+ values = percentages)),
+ sequential_mode = TRUE)
Now you can start the simulation.
+
+ogs6_ens_big$run_simulation()
+lapply(ogs6_ens_big$ensemble, ogs6_read_output_files)
This will take a short time. As soon as the simulations are done, we can extract the point data much like we did before. This time we want to plot the point x coodinates on the x axis so we’re leaving out point_ids
to get all points. Also we just want the data from the last timestep.
+# Get combined dataframe
+per_por_sto_df <-
+ ogs6_ens_big$get_point_data(
+ keys = c("pressure"),
+ start_at_timestep = ogs6_ens_big$ensemble[[1]]$pvds[[1]]$last_timestep)
Plotting time! Since we set sequential_mode
to TRUE
, the dataframe we just created contains a name
column which allows us to group by parameters. Because we’ve also set percentages_mode
to TRUE
, it also has a column perc
which allows us to group by percentages. Now we can simply use a facet grid to plot.
+# Make plot
+ggplot(per_por_sto_df,
+ aes(x = x,
+ y = -pressure, # Flip pressure because source term was positive
+ group = perc)) +
+ geom_point(aes(color = as.factor(perc))) +
+ xlab("Radius (m)") +
+ ylab("Head (m)") +
+ labs(color = "%") +
+ facet_grid(cols = vars(name),
+ labeller = as_labeller(c(per = "permeability",
+ por = "porosity",
+ sto = "storage")))
Ta-Daa! We can see permeability
has the most influence on the pressure. Though they may seem suspicious, porosity
and storage
are being plotted correctly - the points are just being placed right on top of each other. Since porosity
can’t go over the value 1
which was the original value, our value vector only went from -50% to 0% which is why the line colors of porosity
and storage
differ. Maybe we want to try and use a logarithmic approach for storage
. This won’t work with the built-in functionality of OGS6_Ensemble
so we’ll set up our Ensemble a little differently.
+# Calculate log value
+log_val <- log(as.numeric(
+ ogs6_obj$media[[1]]$properties[[4]]$value),
+ base = 10)
+
+# Apply changes to log value
+log_vals <- vapply(percentages, function(x){
+ log_val + (log_val * (x / 100))
+}, FUN.VALUE = numeric(1))
+
+# Transfer back to non-logarithmic scale
+back_transf_vals <- 10^log_vals
+
+# Change sim_path to fit your system
+ogs6_obj$sim_path <- paste0(testdir_path, "/axisym_theis_sim_log_storage")
+
+# Set up new ensemble
+ogs6_ens_sto <-
+ OGS6_Ensemble$new(
+ ogs6_obj = ogs6_obj,
+ parameters =
+ list(
+ sto = list(
+ ogs6_obj$media[[1]]$properties[[4]]$value,
+ values = back_transf_vals)
+ ),
+ percentages_mode = FALSE,
+ sequential_mode = TRUE
+ )
As before, we can run the simulation right away.
+
+ogs6_ens_sto$run_simulation()
+lapply(ogs6_ens_sto$ensemble, ogs6_read_output_files)
Let’s check if we can observe any influence of storage
on pressure
now.
+# Get combined dataframe
+sto_df <-
+ ogs6_ens_sto$get_point_data(
+ keys = c("pressure"),
+ start_at_timestep = ogs6_ens_sto$ensemble[[1]]$pvds[[1]]$last_timestep)
+
+# Supply percentages manually since we couldn't use `percentages_mode`
+percs <- vapply(sto_df$sim_id,
+ function(x){percentages[[x]]},
+ FUN.VALUE = numeric(1))
+
+ggplot(sto_df,
+ aes(x = x,
+ y = -pressure)) +
+ geom_point(aes(color = as.factor(percs))) +
+ xlab("Radius (m)") +
+ ylab("Head (m)") +
+ labs(color = "%")
We will consider the following parameters for our sensitivity analysis:
+permeability
porosity
slope
First, we create a simulation object to base our ensemble on and read in the .prj
file. This time we want to specify that an output file only gets written at the last timestep.
+# Change this to fit your system
+sim_path <- paste0(testdir_path, "/theis_sim")
+
+ogs6_obj <- OGS6$new(sim_name = "theis",
+ sim_path = sim_path)
+
+# Change this to fit your system
+prj_path <- system.file("extdata/benchmarks/theis_well_pumping/",
+ "theis.prj", package = "r2ogs6")
+
+read_in_prj(ogs6_obj, prj_path, read_in_gml = T)
+
+# Increase each_steps
+ogs6_obj$time_loop$output$timesteps$pair$each_steps <- 200
+# Assign percentages
+percentages <- c(-50, -10, -1, 0, 1, 10, 50)
+
+ogs6_ens_theis_2 <-
+ OGS6_Ensemble$new(
+ ogs6_obj = ogs6_obj,
+ parameters =
+ list(
+ per = list(ogs6_obj$parameters[[3]]$values,
+ values = percentages),
+ por = list(ogs6_obj$parameters[[2]]$value,
+ values = percentages),
+ slo = list(
+ ogs6_obj$media[[1]]$phases[[1]]$properties[[1]]$independent_variable[[2]]$slope,
+ values = percentages)
+ ),
+ sequential_mode = TRUE
+ )
Now you can start the simulation.
+
+ogs6_ens_theis_2$run_simulation()
+lapply(ogs6_ens_theis_2$ensemble, ogs6_read_output_files)
When the simulations have run, we can extract and plot the results like before. To avoid cluttering the plot, we only extract the pressure
values for a single line. For this, we get the IDs of all points on the x axis.
+# Extract point ids
+get_point_ids_x <- function(points){
+ x_axis_ids <- numeric()
+
+ for(i in seq_len(dim(points)[[1]])) {
+ if (points[i, ][[2]] == 0 && points[i, ][[3]] == 0) {
+ x_axis_ids <- c(x_axis_ids, (i - 1))
+ }
+ }
+
+ return(x_axis_ids)
+}
+
+point_ids_x <- get_point_ids_x(
+ ogs6_ens_theis_2$ensemble[[1]]$pvds[[1]]$OGS6_vtus[[1]]$points)
+
+# Get combined dataframe
+per_por_slo_df <-
+ ogs6_ens_theis_2$get_point_data(
+ point_ids = point_ids_x,
+ keys = c("pressure"),
+ start_at_timestep = ogs6_ens_theis_2$ensemble[[1]]$pvds[[1]]$last_timestep)
+# Make plot
+ggplot(per_por_slo_df,
+ aes(x = x,
+ y = pressure / 9806.65, # 1mH2O = 9806.65 kPa
+ group = perc)) +
+ geom_point(aes(color = as.factor(perc))) +
+ xlab("Radius (m)") +
+ ylab("Absenkung (m)") +
+ labs(color = "%") +
+ facet_grid(cols = vars(name),
+ labeller = as_labeller(c(per = "permeability",
+ por = "porosity",
+ slo = "slope"
+ )))
Let’s take a closer look at permeability
.
+per_df <- subset(per_por_slo_df, name == "per")
+
+# Make plot
+ggplot(per_df,
+ aes(x = x,
+ y = pressure)) +
+ geom_point(aes(color = as.factor(perc))) +
+ xlab("Radius (m)") +
+ ylab("Head (m)") +
+ labs(color = "%")
Maybe we want to try and use a logarithmic approach for slope
. This won’t work with the built-in functionality of OGS6_Ensemble
so we’ll set up our Ensemble a little differently.
+# Calculate log value
+log_val <- log(as.numeric(
+ ogs6_obj$media[[1]]$phases[[1]]$properties[[1]]$independent_variable[[2]]$slope),
+ base = 10)
+
+# Apply changes to log value
+log_vals <- vapply(percentages, function(x){
+ log_val + (log_val * (x / 100))
+}, FUN.VALUE = numeric(1))
+
+# Transfer back to non-logarithmic scale
+back_transf_vals <- 10^log_vals
+
+# Change sim_path to fit your system
+ogs6_obj$sim_path <- paste0(testdir_path, "/theis_sim_log_slope")
+
+# Set up new ensemble
+ogs6_ens_slo <-
+ OGS6_Ensemble$new(
+ ogs6_obj = ogs6_obj,
+ parameters =
+ list(
+ slo = list(
+ ogs6_obj$media[[1]]$phases[[1]]$properties[[1]]$independent_variable[[2]]$slope,
+ values = back_transf_vals)
+ ),
+ percentages_mode = FALSE,
+ sequential_mode = TRUE
+ )
As before, we can run the simulation right away.
+
+ogs6_ens_slo$run_simulation()
+lapply(ogs6_ens_slo$ensemble, ogs6_read_output_files)
Let’s check if we can observe any influence of slope
on pressure
now.
+# Filter point ids
+point_ids_x <- get_point_ids_x(
+ ogs6_ens_slo$ensemble[[1]]$pvds[[1]]$OGS6_vtus[[1]]$points)
+
+# Get combined dataframe
+slo_df <-
+ ogs6_ens_slo$get_point_data(
+ point_ids = point_ids_x,
+ keys = c("pressure"),
+ start_at_timestep = ogs6_ens_slo$ensemble[[1]]$pvds[[1]]$last_timestep)
+
+# Supply percentages manually since we couldn't use `percentages_mode`
+percs <- vapply(slo_df$sim_id,
+ function(x){percentages[[x]]},
+ FUN.VALUE = numeric(1))
+
+ggplot(slo_df,
+ aes(x = x,
+ y = pressure / 9806.65)) + # 1mH2O = 9806.65 kPa
+ geom_point(aes(color = as.factor(percs))) +
+ xlab("Radius (m)") +
+ ylab("Head (m)") +
+ labs(color = "%")
The OGS6_Ensemble
class is a useful tool to set up ensemble runs for sensitivity analyses. In this vignette, we learned how to create OGS6_Ensemble
objects. We looked at how the parameters sequential_mode
and percentages_mode
influence how our ensemble object is initialised. We started simulations via OGS6_Ensemble$run_simulation()
and extracted information from the output files to plot them.
user_workflow_vignette.Rmd
This guide assumes you have r2ogs6
and its dependencies installed. If that’s not the case, please take a look at the installation instructions provided in the README.md
file of the repository.
After loading r2ogs6
, we first need to set the package options so it knows where to look for OpenGeoSys 6.
+# Set path for OpenGeoSys 6
+options("r2ogs6.default_ogs6_bin_path" = "your_ogs6_bin_path")
To represent a simulation object, r2ogs6
uses an R6
class called OGS6
. If you’re new to R6
objects, don’t worry. Creating a simulation object is easy. We call the class constructor and provide it with some parameters:
sim_name
The name of your simulation
sim_path
All relevant files for your simulation will be in here
+# Change this to fit your system
+# sim_path <- system.file("extdata/benchmarks/flow_no_strain",
+# package = "r2ogs6")
+sim_path <- tempdir()
+ogs6_obj <- OGS6$new(sim_name = "my_simulation",
+ sim_path = sim_path)
And that’s it, we now have a simulation object.
+From here on there’s two ways you can define the simulation parameters. Either you load a benchmark file or you define your simulation manually.
+The quickest and easiest way to define a simulation is by using an already existing benchmark. If you take a look at the OpenGeoSys documentation, you’ll find plenty of benchmarks to choose from along with a link to their project file on GitLab at the top of the respective page.
+For demonstration purposes, I will use a project from the HydroMechanics
benchmarks, which can be found here.
+# Change this to fit your system
+#prj_path <- paste0(sim_path, "/flow_no_strain.prj")
+prj_path <- system.file("extdata/benchmarks/flow_no_strain/flow_no_strain.prj",
+ package = "r2ogs6")
+read_in_prj(ogs6_obj, prj_path = prj_path, read_in_gml = T)
NOTE: r2ogs6
has not been tested with every existing benchmark. Due to the large number of input parameters, you might encounter cases where the import fails.
Setting up your own simulation is possible too.
+Since there’s plenty of required and optional input parameters, you might want to call get_status()
occasionally to get a brief overview of your simulation. This tells you which input parameters are missing before you can run a simulation.
+# Call on the OGS6 object (note the R6 style)
+ogs6_obj$get_status()
+#> ✓ 'processes' has at least one element
+#> ✓ 'time_loop' is defined
+#> ✓ 'nonlinear_solvers' has at least one element
+#> ✓ 'linear_solvers' has at least one element
+#> ✓ 'parameters' has at least one element
+#> ✓ 'process_variables' has at least one element
+#> ✗ 'mesh' is defined
+#> ✗ 'geometry' is defined
+#> ✓ 'media' has at least one element
+#> ✗ 'test_definition' has at least one element
+#> ✗ 'curves' has at least one element
+#> ✓ 'meshes' has at least one element
+#> ✗ 'local_coordinate_system' is defined
+#> ✗ 'search_length_algorithm' is defined
+#> ✗ 'chemical_system' is defined
+#> ✗ 'python_script' is defined
+#> ✗ 'insitu' is definedYour OGS6 object has all necessary components.
+#> You can try calling ogs6_run_simulation().Note that this calls more validation functions, so you may not be done just yet.
Since we haven’t defined anything so far, you’ll see a lot of red there. But the results gave us a hint what we can add. We’ll go from there and try to find out more about the possible input data. Say we want to find out more about process
objects.
+# To take a look at the documentation, use ? followed by the name of a class
+?prj_process
As a rule of thumb, classes are named with the prefix prj_
followed by their XML tag name in the .prj
file. The only exceptions to this rule are subclasses where this would lead to duplicate class names. The class prj_time_loop
for example contains a subclass representing a process
child element which is not to be confused with the process
children of the first level processes
node directly under the root node of the .prj
file. Because of this, that subclass is named prj_tl_process
. Let’s try adding something now.
To add data to our simulation object, we use OGS6$add()
. We can use this method with any top level .prj
element, which means we’re not limited to prj_parameter
objects.
+# Add a parameter
+ogs6_obj$add(prj_parameter(name = "E",
+ type = "Constant",
+ value = 1e+10))
+
+
+# Add a process variable
+ogs6_obj$add(
+ prj_process_variable(
+ name = "pressure",
+ components = 1,
+ order = 1,
+ initial_condition = "pressure0",
+ boundary_conditions = list(
+ boundary_condition = prj_boundary_condition(
+ type = "Neumann",
+ parameter = "flux_in",
+ geometrical_set = "cube_1x1x1_geometry",
+ geometry = "left",
+ component = 0
+ )
+ )
+ )
+)
Since I already read in a .prj
file earlier, I won’t run the above snippet. If you’d like a complete example of manually defining simulation parameters, there’s a script flow_free_expansion.R
in the examples/workflow_demos
folder.
As soon as we’ve added all necessary parameters, we can try starting our simulation. This will run a few additional checks and then start OpenGeoSys 6. If write_logfile
is set to FALSE
, the output from OpenGeoSys 6 will be shown on the console.
+
+ogs6_run_simulation(ogs6_obj)
After our simulation is finished, we might want to plot some results. But how do we retrieve them? If all went as expected, we don’t need to call an extra function for that because ogs6_run_simulation()
already calls ogs6_read_output_files()
internally. We only need to decide what information we want to extract. Say we’re interested in the pressure
Parameter from the last timestep. For this easy example, only one .pvd
file was produced.
+ogs6_read_output_files(ogs6_obj)
+# Extract relevant info into dataframe
+result_df <- ogs6_obj$pvds[[1]]$get_point_data(keys = c("pressure"))
+result_df <- result_df[(result_df$timestep!=0),]
+
+# Plot results
+ggplot(result_df,
+ aes(x = x,
+ y = y,
+ color = pressure)) +
+ geom_point() +
+ #geom_raster(interpolate = T)+
+ #geom_contour_filled()+
+ xlab("x coordinate") +
+ ylab("y coordinate") +
+ theme_bw()
Similar to the idea with .pvd
output, hdf5
files are automatically referenced under $h5s
if returned by the simulation. Here, we have added a file artificially from the benchmark library for demonstration.
+ogs6_obj$h5s
+#> [[1]]
+#> OGS6_h5
+#> h5 path:
+#> /work/ufz/r2ogs6/inst/extdata/benchmarks/EllipticPETSc/cube_1e3_np3.h5
+#>
+#> # h5 file structure ------------------------------------------------------
+#> group name otype dclass dim
+#> 0 / t_0 H5I_GROUP
+#> 1 /t_0 D1_left_front_N1_right H5I_DATASET FLOAT 1895
+#> 2 /t_0 Linear_1_to_minus1 H5I_DATASET FLOAT 1895
+#> 3 /t_0 MaterialIDs H5I_DATASET INTEGER 1233
+#> 4 /t_0 geometry H5I_DATASET FLOAT 3 x 1895
+#> 5 /t_0 pressure H5I_DATASET FLOAT 1895
+#> 6 /t_0 topology H5I_DATASET INTEGER 11097
+#> 7 /t_0 v H5I_DATASET FLOAT 3 x 1895
+#> 8 / t_1 H5I_GROUP
+#> 9 /t_1 pressure H5I_DATASET FLOAT 1895
+#> 10 /t_1 v H5I_DATASET FLOAT 3 x 1895
As can be seen, hdf5 files have a very particular structure. To work with the data, a simple method get_h5()
allows to access the different data elements as a very raw starting point for post processing the data.
+h5_list <- ogs6_obj$h5s[[1]]$get_h5("/")
Other functions such as HDF5 handles for dataset processing can be used directly from the rhdf5 package, of course. The path to the hd5 file is available as an active field h5_path
that can be accessed or changed in the OGS6 object.
+example_h5 <- rhdf5::H5Fopen(ogs6_obj$h5s[[1]]$h5_path)
+example_h5
+#> HDF5 FILE
+#> name /
+#> filename
+#>
+#> name otype dclass dim
+#> 0 t_0 H5I_GROUP
+#> 1 t_1 H5I_GROUP
+str(example_h5$t_0)
+#> List of 7
+#> $ D1_left_front_N1_right: num [1:1895(1d)] 1 1 1 1 1.01 ...
+#> $ Linear_1_to_minus1 : num [1:1895(1d)] 1 0.8 0.6 1 0.8 0.6 1 0.8 0.6 1 ...
+#> $ MaterialIDs : int [1:1233(1d)] 0 0 0 0 0 0 0 0 0 0 ...
+#> $ geometry : num [1:3, 1:1895] 0 0 0 0.1 0 0 0.2 0 0 0 ...
+#> $ pressure : num [1:1895(1d)] 0 0 0 0 0 0 0 0 0 0 ...
+#> $ topology : int [1:11097(1d)] 9 0 1 4 3 49 50 53 52 9 ...
+#> $ v : num [1:3, 1:1895] 0 0 0 0 0 0 0 0 0 0 ...
+rhdf5::h5closeAll()
If the file has a reasonably “clean” structure, a more convenient way of importing the data is using the method get_df
that returns a tibble table.
+df <- ogs6_obj$h5s[[1]]$get_df(group = "/t_0", names = "pressure")
+df
+#> # A tibble: 1,895 × 5
+#> x y z time pressure
+#> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 0 0 0 0 0
+#> 2 0.1 0 0 0 0
+#> 3 0.2 0 0 0 0
+#> 4 0 0.1 0 0 0
+#> 5 0.1 0.1 0 0 0
+#> 6 0.2 0.1 0 0 0
+#> 7 0 0.2 0 0 0
+#> 8 0.1 0.2 0 0 0
+#> 9 0.2 0.2 0 0 0
+#> 10 0 0.3 0 0 0
+#> # … with 1,885 more rows
If we want to run not one but multiple simulations, we can use the simulation object we just created as a blueprint for an ensemble run. The workflow for this is described in detail here.
+Another feature of r2ogs6
is benchmark script generation. For this, there are two functions.
ogs6_generate_benchmark_script()
creates an R script from a .prj
file
ogs6_generate_benchmark_scripts()
is a wrapper for the former. Instead of a single .prj
file path, it takes a directory path as its argument.
Let’s look at the parameters for ogs6_generate_benchmark_script()
first. Say we have a project file sim_file.prj
we want to generate a script from. Please, make sure that all the directories your are referencing exist.
+# The path to the project file you want to generate a script from
+prj_path <- "your_path/sim_file.prj"
+
+# The path you want to save the simulation files to
+sim_path <- "your_sim_directory/"
+
+# The path to your `ogs.exe` (if not already specified in `r2ogs6` options)
+ogs6_bin_path <- "your_ogs6_bin_path/"
+
+# The path you want your benchmark script to be saved to
+script_path <- "your_script_directory/"
Now that we have defined our parameters, we can generate the benchmark script.
+
+ogs6_generate_benchmark_script(prj_path = prj_path,
+ sim_path = sim_path,
+ ogs6_bin_path = ogs6_bin_path,
+ script_path = script_path)
On the other hand, if we want to generate R scripts from multiple (or all) benchmarks, we can use the wrapper function ogs6_generate_benchmark_scripts()
. Its parameters are basically the same, only this time we supply it with a directory path instead of a .prj
path to start from.
You can download the benchmarks (or the subfolder you need) from here and then set path
to their location on your system.
+# The path to the directory you want to generate R scripts from
+path <- "path/to/ogs/Tests/Data/Elliptic/"
+
+# The path you want to save the simulation files to
+sim_path <- "your_sim_directory/"
+
+# The path you want your benchmark scripts to be saved to
+script_path <- sim_path
+
+# Optional: Use if you want to start from a specific `.prj` file
+starting_from_prj_path <- ""
+
+# Optional: Use if you want to skip specific `.prj` files
+skip_prj_paths <- character()
+
+# Optional: Use if you want to skip specific `.prj` files
+skip_prj_paths <- character()
+
+# Optional: Use if you want to restrict scripting to specific `.prj` files
+only_prj_files <- character()
And we’re set! Note that ogs6_generate_benchmark_scripts()
will reconstruct the structure of the folder your benchmarks are stored in, e. g. if there’s a file path/a/file.prj
, you will find the corresponding R script in sim_path/a/file.R
.
+ogs6_generate_benchmark_scripts(path = path,
+ sim_path = sim_path,
+ script_path = script_path,
+ starting_from_prj_path = starting_from_prj_path,
+ skip_prj_paths = skip_prj_paths,
+ only_prj_files = only_prj_files)
With this, we can generate scripts from all benchmarks in a single call. Of course you can modify path
to your liking if you’re only interested in generating scripts from certain subfolders.
Furthermore, you can restrict the script generation to benchmarks that used as test in OGS 6.
+
+# extract *.prj files that are used as tests
+rel_testbm_paths <- get_benchmark_paths("path/to/ogs-source-code/ProcessLib/")
+rel_testbm_paths <- sapply(rel_testbm_paths, basename)
+
+ogs6_generate_benchmark_scripts(path = path,
+ sim_path = sim_path,
+ script_path = script_path,
+ only_prj_files = rel_testbm_paths)
NOTE: New benchmarks and .prj
parameters are constantly being added to OGS6. If a benchmark contains parameters that have not been added to r2ogs6
yet, the script generation functions will not work. If this is the case, they will be skipped and the original error message will be displayed in the console.