> For the complete documentation index, see [llms.txt](https://iddynamics.gitbook.io/flepimop/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://iddynamics.gitbook.io/flepimop/more/setting-up-the-model-and-post-processing/create-a-post-processing-script.md).

# Create a post-processing script

These scripts are run automatically after an inference run

Some information to consider if you'd like your script to be run automatically after an inference run ;

* Most R/python packages are installed already installed. Try to run your script on the conda environment defined on the [submission page](/flepimop/how-to-run/advanced-run-guides/running-on-a-hpc-with-slurm.md) (or easier if you are not set up on MARCC, ask me)
* There will be some variables set in the environment. These variables are:
  * `$CONFIG_PATH` the path to the configuration fil ;
  * `$FLEPI_RUN_INDEX` the run id for this run (e.g \``CH_R3_highVE_pesImm_2022_Jan29`\`
  * `$JOB_NAME` this job name (e.g `USA-20230130T163847_inference_med`)
  * `$FS_RESULTS_PATH` the path where lies the model results. It's a folder that contains the model\_ouput/ as a subfolder
  * `$FLEPI_PATH` path of the flepiMoP repository.
  * `$PROJECT_PATH` path of the Data directory (e.g Flu\_USA or COVID19\_USA).
  * Anything you ask can theoretically be provided here.
* The script must run without any user intervention.
* The script is run from $PROJECT\_PATH.
* Your script lies in the flepiMoP directory (preferably) or it's ok if it is in a data directory if it makes sense ;
* It is run on a 64Gb of RAM multicore machine. All scripts combined must complete under 4 hours, and you can use multiprocessing (48 cores)
* Outputs (pdf, csv, html, txt, png ...) must be saved in a directory named `pplot/` (you can assume that it exists) in order to be sent to slack by FlepiBot 🤖 after the run.
* an example postprocessing script (in python) is [here](https://github.com/HopkinsIDD/COVIDScenarioPipeline/blob/main-flu-subfix2/scripts/postprocess_auto.py).
* You can test your script on MARCC on a run that is already saved in `/data/struelo1/flepimop-runs` or I can do it for you.
* Once your script works, add (or ask to add) the command line to run in file `batch/postprocessing_scripts.sh` [(here)](https://github.com/HopkinsIDD/COVIDScenarioPipeline/blob/main-flu-subfix2/batch/postprocessing-scripts.sh) between the START and END lines, with a little comment about what your script does.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://iddynamics.gitbook.io/flepimop/more/setting-up-the-model-and-post-processing/create-a-post-processing-script.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.