Research analysis data for the improved FEMA P-58 residual drift generation project.

# using venv
$ python -m venv {path/to/virtual_environment}
$ source {path/to/virtual_environment}/bin/activate
$ python -m pip install -r requirements.txt

# using conda
$ conda create -n rid_prj -c conda-forge python=3.12 -y
$ conda activate rid_prj
$ python -m pip install -r requirements.txt

You would then have to configure your editor to use the interpreter from the newly created environment. (Emacs) (pycharm) (spyder)
The interpreter is always assumed to be launched at the project root (the directory containing src, not src itself).

Tearing down the enviornment, if needed:

# using venv
$ rm -r {path/to/virtual_environment}

# using conda
conda remove -n rid_prj --all

src/ contains the source code.

src/validation contains code that is not meant to reproduce the results of the study, but was utilized while developing the main code to take incremental steps towards a goal or to troubleshoot issues.

data/ contains the time-history analysis data we use.

results contains analysis results, such as fit parameters or generated figures, and should not be version-controled.
We use the following directory structure: results/{result-category}/{data_gathering_approach}/{method}/{result-filename}.
{result-category} can be any of parameters, tables, figures.
{data_gathering_approach} can be any of separate_directions, bundled_directions.
{method} corresponds to the fitting methods we examine.
{result-filename} is the file name for a type of result, and it can be the same for different methods.

doc/ contains the files used to generate documents associated with the project, such as the poster presentation, manuscripts and any other derivative work.

data/ and results/ are not version controlled, but the contents are tracked with DVC.

After cloning the repository and setting up the environment, issue the following command to pull the contents:

$ dvc pull

After making changes, they should be added with DVC and then committed with git.

# changes in data/
$ dvc add data

# changes in results/
$ dvc add results

$ dvc push
$ git add {changed-dvc-files}
$ git commit -m 'DVC - update results'

This section will describe in what order to execute the code to reproduce all of the project's analysis results. Note that the results should already be available using DVC.

Set the PYTHONPATH variable

export PYTHONPATH=$PYTHONPATH:$(pwd)

1. Fit all the models. Creates parameters.parquet and models.picle files in results/parameters/{method}/.

$ python src/fit_models.py

2. Create plots that help assess the quality of the fit. Creates fit_{system}_{stories}_{rc}.pdf files in results/figures/{method}/.

$ python src/plot_fit.py