This document illustrates the simulation workflow of ERAHUMED. Specifically, we will cover:
- How to setup and run a simulation.
- How to extract and analyze simulation results.
This guide is addressed to users working with the command-line (i.e. R) interface of erahumed.
ERAHUMED simulation can be run with:
sim <- erahumed_simulation()
sim
#> A ERAHUMED simulation.
#>
#> Start date: 2020-01-01
#> End date: 2020-12-31
#>
#> Need help extracting simulation outputs? Check `?get_results`.The command above runs a simulation with the default model
parameters. These can be customized through the arguments of
erahumed_simulation(), for instance:
sim2 <- erahumed_simulation(
variety_prop = c(J.Sendra = 0.6, Bomba = 0.2, Clearfield = 0.2),
ideal_flow_rate_cm = 10,
seed = 841
)
#> Warning in generate_clusters_variety(variety_prop): Surface proportion
#> allocated to 'Clearfield' was too high. Reduced to 0.162698421372703The full set of simulation parameters is documented in
?erahumed_simulation (see here
for a table format).
Once we are ready with our simulation setup, in order to actually run the simulation we use:
Simulation results are extracted as follows:
lake_hydrology_df <- get_results(sim, component = "hydrology", element = "lake")
cluster_hydrology_df <- get_results(sim, component = "hydrology", element = "cluster")
cluster_exposure_df <- get_results(sim, component = "exposure", element = "cluster")These are provided in the form of data.frames, for
instance:
head(cluster_hydrology_df)
#> ideal_height_eod_cm ideal_irrigation ideal_draining petp_cm area_m2
#> 1 20 TRUE TRUE -0.058 114881.78
#> 2 20 TRUE TRUE -0.058 116539.90
#> 3 20 TRUE TRUE -0.058 154730.35
#> 4 20 TRUE TRUE -0.058 163789.56
#> 5 20 TRUE TRUE -0.058 83016.51
#> 6 20 TRUE TRUE -0.058 106260.07
#> capacity_m3_s date element_id ditch_element_id seed_day
#> 1 0.05741888 2020-01-01 02_Carrera_del_Saler0-2_0 d2 -110
#> 2 0.05741888 2020-01-01 03_Petxinar0-3_2 d2 -110
#> 3 0.05741888 2020-01-01 03_Petxinar0-3_3 d2 -110
#> 4 0.05741888 2020-01-01 03_Petxinar1-3_1 d2 -110
#> 5 0.05741888 2020-01-01 03_Petxinar1-3_2 d2 -110
#> 6 0.05741888 2020-01-01 03_Petxinar1-3_3 d2 -110
#> tancat variety height_sod_cm irrigation draining ideal_diff_flow_cm
#> 1 TRUE J.Sendra 20 TRUE TRUE 0.058
#> 2 TRUE J.Sendra 20 TRUE TRUE 0.058
#> 3 TRUE J.Sendra 20 TRUE TRUE 0.058
#> 4 TRUE Clearfield 20 TRUE TRUE 0.058
#> 5 TRUE Clearfield 20 TRUE TRUE 0.058
#> 6 TRUE Bomba 20 TRUE TRUE 0.058
#> ideal_inflow_cm ideal_outflow_cm outflow_m3_s outflow_cm inflow_cm
#> 1 5 4.942 0 0 0.058
#> 2 5 4.942 0 0 0.058
#> 3 5 4.942 0 0 0.058
#> 4 5 4.942 0 0 0.058
#> 5 5 4.942 0 0 0.058
#> 6 5 4.942 0 0 0.058
#> inflow_m3_s height_eod_cm plan_delay
#> 1 0.0007711971 20 0
#> 2 0.0007823281 20 0
#> 3 0.0010386991 20 0
#> 4 0.0010995133 20 0
#> 5 0.0005572868 20 0
#> 6 0.0007133199 20 0From here on, the analysis may proceed in the way you find more
convenient. For instance, in the chunk below I create a plot of water
levels for a set of clusters with similar features, using
dplyr and ggplot2:
library(dplyr)
library(ggplot2)
ditch <- "d4"
tancat <- FALSE
variety <- "Clearfield"
clusters_df <- cluster_hydrology_df |>
filter(ditch == !!ditch, tancat == !!tancat, variety == !!variety)
avg_df <- clusters_df |>
group_by(date) |>
summarise(height_eod_cm = mean(height_eod_cm))
ggplot() +
geom_line(
data = clusters_df,
mapping = aes(x = date, y = height_eod_cm, group = element_id),
color = "black", linewidth = 0.1, alpha = 0.2) +
geom_line(
data = avg_df,
mapping = aes(x = date, y = height_eod_cm),
color = "black"
) +
xlab("Date") + ylab("Height [cm]") +
ggtitle("Cluster simulated water levels",
paste("Ditch:", ditch, "- Tancat:", tancat, "- Variety:", variety)
)
Further information
Further details will appear in this and possibly other vignettes. For specific problems, you can file an issue on Github.