anchor.diff:      - the outputs are saved in the 'output' dir. If it does not exist, the code
         will create it at the beginning of the simulation.
   
  -The code can be run in a number of modes. The relevant options are:
  -
  -  -d INPUT_DIRECTORY, --input_directory INPUT_DIRECTORY
  +
  +## Running the Application
  +
  +The application can be run in a number of modes. The relevant options are:
  +
  +  -d <path_to_input_dir>, --input_directory <path_to_input_dir>
     -s, --simulation
     -f, --fit
     -l, --loss_analysis
   
  -Following code snippets assume that it is being run from the root directory
  -of the SIRA code, and the model of interest is in the location
  -`sira/scenario_dir/ci_model_foo`.
  +The following code snippets assume that it is being run from the root 
  +directory of the SIRA code, and the model of interest is in the location 
  +`sira/scenario_dir/ci_model_x`.
   
   The following code runs the simulation and the post processing simultanrously:
   
  -    $ python sira -d scenario_dir/ci_model_foo -sfl
  +    $ python sira -d scenario_dir/ci_model_x -sfl
   
   To run only the monte carlo simulation without post-processing:
   
  -    $ python sira -d scenario_dir/ci_model_foo -s
  +    $ python sira -d scenario_dir/ci_model_x -s
   
   To run for both the model fitting and the loss analysis code:
   
  -    $ python sira -d scenario_dir/ci_model_foo -fl
  +    $ python sira -d scenario_dir/ci_model_x -fl
   
   Note that the fitting and loss analysis steps require the initial simulation
   to have been run first so that it has the initial output data to perform the
   analysis on.
   
  +### Run Option #1: Run a simulation and destroy the container when done
  +
  +The following command simulataneously does the following:
  +bind mounts a volume in docker, creates a container in interactive mode, 
  +runs a simulation, then destroys the container after simulation ends.
  +
  +    $ docker run -it --rm -v /abs/local/path/<scenario_dir>:/<scenario_dir> \
  +        siraimg:latest \
  +        python sira -d <scenario_dir> -sfl --aws
  +
  +### Run Option #2: Build a container for reuse / experimentation
  +
  +First, build a docker container from the prebuilt image.
  +
  +    $ docker create --name=sira_x -it siraimg:latest
  +
  +Then start and attach the container:
  +
  +    $ docker start sira_x
  +    $ docker attach sira_x
  +
  +It is possible to combine the above steps in one:
  +
  +    $ docker start -a -i sira_x
  +
  +Run the sira code for the scenario in the specified directory:
  +
  +    $ python sira -d /path/to/scenario_dir -sfl
  +
  +From outside of docker, on a terminal, use the following command to
  +copy the project folder from container to host:
  +
  +    $ docker cp $(docker ps -alq):/from/path/in/container /to/path/in/host/
  +
   
   ## Testing
   
  -**The tests are currently not working and being updated.**
  -
  -To run tests use unittest. Move into sira folder:
  +To run tests use `unittest`. Move into sira directory:
   
       $ cd sira
       $ python -m unittest discover tests
  +
  +Alternately, you can just run `pytest` from the top of the sira directory:
  +
  +    $ pytest
   
   If you are using docker as described above, you can do this within the sira
   container.
  
positive.code: :toc: macro
  :toc-title:
  :toclevels: 99
  
  # SIRA
  
  
negative.code: 
      $ docker build -t siraimg . --build-arg CACHE_DATE="$(date)"
  
  ### Required Directory Structure
  
  To set up a scenario or impact simulation project, SIRA expects the following
  directory structure:
  
  ```
      scenario_dir/
      └── model_x
          │
          ├── input
          │   ├── config_assetx.json
          │   └── model_assetx.json
          │
          └── output
              ├── ...
              └── ...
  ```
  
  Explanation of the required structure is as follows:
  
      - 'scenario directory' - it can be named anything
      - within the scenario directory, there must exist a uniquely named
  