more doc updates

Author: ericmpham

docs/src/perturbed_dycoms_rf02.md

@@ -115,14 +115,20 @@ Finally, we need to update type_getters.jl with our new keyword arguments as wel
 With this step complete, we are now ready to pass a new keyword argument that we defined ourselves to modify or change the initial conditions. The scripts currently contained in /examples/perturbed_dycoms_rf02 contain code for running the ensemble as well as post-processing/plotting capabilities.
 
 # Creating & Running Ensembles
+All example scripts referenced are stored in examples/perturbed_dycoms_rf02:
+
+```
+script_folder = joinpath(pkgdir(CA), "examples", "perturbed_dycoms_rf02")
+```
+
 The script `pipeline.jl` handles this entire step without parallelized runs, but the steps can also be performed individually. 
 
 The recommended workflow is the create multiple copies of the .yml files and label them according to the initial conditions used. For example, the default file might look like `prognostic_edmfx_dycoms_rf02_column_qtot0_9.45_theta0_288.3_thetai_295.0_zi_795.0_prescribedN_1.0e8.yml`.
 
 We can use `make_yaml.jl` to generate multiple different configurations with ease. To do so, provide the function `make_yamls()` with a default file for which to copy, and an output path. This script uses nested for loops to generate every possible combination of parameters, and they can be altered directly in the file. This might look like:
 
 ```
-include(joinpath(pkgdir(CA),"LWP_N_scripts", "make_yaml.jl"))
+include(joinpath(script_folder, "make_yaml.jl"))
 
 default_data_path = default_prog_2M
 output_dir = prognostic_2M_config
@@ -132,20 +138,21 @@ make_yamls(default_data_path, output_dir, is_prog=true)
 To use these configuration files, we can either parallelize or run them serially. If parallelization is desired, run: 
 
 ```
-julia -p <num_processors> parallel_driver.jl
+julia -p <num_processors> ./examples/perturbed_dycoms_rf02/parallel_driver.jl
 ```
 
 Be sure that the paths, which are handled by `all_paths.jl`, are referring to the right set of simulations. This example focuses in on prognostic EDMF+2M simulations, but the scripts have implementation for diagnostic EDMF as well as 1-moment microphysics as well.
 
 # Visualizing Ensemble Output
-After the simulations are done being ran, we can use `process_plot_outputs.jl` to visualize changes in the liquid water path (LWP) and cloud droplet number concentration (N), in order to recreate the figure above. An example code block to recreate the plot is shown below, but additionally plotting capabilities are demonstrated in `LWP_N_Experiments.ipynb`.
+After the simulations are done being ran, we can use `process_plot_outputs.jl` to visualize changes in the liquid water path (LWP) and cloud droplet number concentration (N), in order to recreate the figure above.
 
 ```
-include(joinpath(pkgdir(CA), "LWP_N_scripts", "process_plot_outputs.jl"))
-output_dir = output_2M
+include(joinpath(script_folder, "process_plot_outputs.jl"))
+
+output_dir = "./output" #output_2M
 all_outputs = make_edmf_vec(output_dir)
 filtered = filter_runs(all_outputs)
-sampled = StatsBase.sample(filtered, 40; replace = false)
+# sampled = StatsBase.sample(filtered, 40; replace=false)
 
-fig = plot_edmf(sampled, is_1M = false, is_time = false, save = false)
+fig = plot_edmf(filtered, is_1M=false, is_time=false)
 ```