From 2aa54ac67bfbd37a97f1dcaad62d9f47675a6153 Mon Sep 17 00:00:00 2001
From: aheinri5 <Anna@netzkritzler.de>
Date: Thu, 18 Feb 2021 13:38:07 +0100
Subject: [PATCH] [docs] data now plotting correctly in vignette
---
vignettes/ensemble_workflow_vignette.Rmd | 45 +++++++++++++++++++-----
1 file changed, 37 insertions(+), 8 deletions(-)
diff --git a/vignettes/ensemble_workflow_vignette.Rmd b/vignettes/ensemble_workflow_vignette.Rmd
index 4cacf5b..d668372 100644
--- a/vignettes/ensemble_workflow_vignette.Rmd
+++ b/vignettes/ensemble_workflow_vignette.Rmd
@@ -223,7 +223,7 @@ We will consider the following parameters for our sensitivity analysis:
### Setup
-First, we create a simulation object to base our ensemble on and read in the `.prj` file.
+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.
```{r}
# Change this to fit your system
@@ -238,6 +238,9 @@ prj_path <- system.file("examples/Theis_well_pumping/benchmark_files/",
"theis.prj", package = "r2ogs6")
read_in_prj(ogs6_obj, prj_path)
+
+# Increase each_steps
+ogs6_obj$time_loop$output$timesteps$pair$each_steps <- 200
```
### Multi-parameter sequential run
@@ -251,13 +254,13 @@ ogs6_ens_theis_2 <-
ogs6_obj = ogs6_obj,
parameters =
list(
- per = list(
+ per = list(ogs6_obj$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),
- por = list(ogs6_obj$media[[1]]$properties[[2]]$value,
- c(-50, -10, -1, 0)),
- slo = list(ogs6_obj$media[[1]]$properties[[4]]$value,
- values = percentages)
+ values = percentages)
),
sequential_mode = TRUE
)
@@ -266,12 +269,23 @@ exit_codes <- ogs6_ens_theis_2$run_simulation()
paste(exit_codes)
```
-When the simulations have run, we can extract and plot the results just like before.
+When the simulations have run, we can extract and plot the results like before. To avoid cluttering the plot, we only extract the `pressure` values for a single line. For this, we get the IDs of all points on the x axis.
```{r}
+# Extract point ids
+all_points <- ogs6_ens_theis_2$ensemble[[1]]$pvds[[1]]$OGS6_vtus[[1]]$points
+x_axis_ids <- numeric()
+
+for(i in seq_len(dim(all_points)[[1]])){
+ if(all_points[i,][[2]] == 0 && all_points[i,][[3]] == 0){
+ x_axis_ids <- c(x_axis_ids, (i-1))
+ }
+}
+
# Get combined dataframe
per_por_slo_df <-
ogs6_ens_theis_2$get_point_data(
+ point_ids = x_axis_ids,
keys = c("pressure"),
start_at_timestep = ogs6_ens_theis_2$ensemble[[1]]$pvds[[1]]$last_timestep)
```
@@ -288,6 +302,21 @@ ggplot(per_por_slo_df,
facet_grid(cols = vars(name))
```
+
+Let's take a closer look at `permeability`.
+
+```{r}
+per_df <- subset(per_por_slo_df, name == "per")
+
+# Make plot
+ggplot(per_df,
+ aes(x = x,
+ y = pressure)) +
+ geom_point(aes(color = as.factor(perc))) +
+ xlab("x point coordinate") +
+ labs(color = "%")
+```
+
## Summary
The `OGS6_Ensemble` class is a useful tool to set up ensemble runs for sensitivity analyses. In this vignette, we learned how to create `OGS6_Ensemble` objects. We looked at how the parameters `sequential_mode` and `percentages_mode` influence how our ensemble object is initialised. We started simulations via `OGS6_Ensemble$run_simulation()` and extracted information from the output files to plot them.
--
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