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Reproducing pyCSEP: A Software Toolkit for Earthquake Forecast Developers

Software
Data

A reproducibility package contains the code and data needed to recreate figures from a published article (Krafczyk et al., 2021). This reproducibility package provides an introduction to pyCSEP and acts as an example for future publications. We refer readers interested in creating their own reproducibility packages to Krafczyk et al. (2021).

We provide the user with options to download a full or lightweight version of the reproducibility package from Zenodo. The full version of the package recreates Figs. 2‒7 from the manuscript. The lightweight version omits Fig. 3 and Fig. 6, because they require a ~24Gb download for the UCERF3-ETAS forecast, which can take a while (~3h) depending on the connection to Zenodo. These figures also require the most computing time to recreate (see Computational effort).

We recommend that users begin with the lightweight version of the package for a quick introduction to pyCSEP and to use the full version to learn about evaluating catalog based forecasts or working with UCERF3-ETAS forecasts. The package is configured to provide turn-key reproducibility of the published results. Users can also interact with scripts to individually recreate each figure.

Table of contents

Instructions for running

If you have obtained the software from Zenodo, you may skip this step. Make sure that the file contents are extracted and navigate to the package directory.

If you are viewing this on GitHub or have not downloaded the code from Zenodo, open a terminal and download the reproducibility package from GitHub with

git clone https://github.com/wsavran/pycsep_esrl_reproducibility.git

Navigate to the newly downloaded directory

cd pycsep_esrl_reproducibility

Prepare the computational environment

Important: Use the Docker instructions to ensure the same computational environment as the publication. The conda instructions are useful for users that wish to extend the package or want to work with pyCSEP outside of this context.

Now you have two options how to run the package:

The easiest way to run the reproducibility package is to run the lightweight version of the package in an environment provided by Docker. If you are interested in working with pyCSEP in more detail, we recommend that you install pyCSEP in a conda environment in the native OS.

For both options we have accompanying scripts that work both under Linux/macOS or Windows.

Easy mode using Docker

You will need to have the Docker runtime environment installed and running on your machine. Some instructions can be found here. The following commands will not work unless the Docker engine is correctly installed on your machine.

If on Linux/maxOS, call:

./run_all.sh

If on Windows, call:

.\run_all.bat

This step does the following things: (1) download and verify the checksum of the downloaded data; (2) build a docker image with the computational environment; and (3) run the reproducibility package.

Note: For best performance on Windows 10/11, Docker should be used with the WSL2 backend instead of the legacy Hyper-V backend---provided your hardware supports it. This can be configured in Docker's Settings > General > 'Use the WSL 2 based engine'. For more information and how to enable the WSL2 feature on your Windows 10/11, see Docker Desktop WSL 2 backend.

To download the full version, call:

./run_all.sh --full

or (if on Windows):

.\run_all.bat --full

When finished, the results and figures will be stored in ./results and ./figures, respectively.

The start_docker.sh or start_docker.bat scripts provide an interactive terminal to re-create individual figures. See below for instructions.

Using conda environment

Installation instructions can be found in the pyCSEP documentation. Warning: this method does not ensure a stable computing environment, because conda provides the newest packages that are compatible with your environment. Please report any issues related to this here.

Create and activate a new conda environment

conda env create -n pycsep_esrl
conda activate pycsep_esrl

Install v0.5.2 of pyCSEP

conda install --channel conda-forge pycsep=0.5.2

Download data from Zenodo

./download_data.sh

or (if on Windows):

.\download_data.bat

Note: to download the 'full' version, append --full to the command (see above)

Run the analysis scripts

The scripts to reproduce the figures in the manuscript are contained in the scripts directory.

cd scripts

Note: Any script must be launched from the scripts directory of the reproducibility package.

Create all figures

To produce all figures from the manuscript that are supported by your downloaded version ('lightweight' or 'full'), run:

python plot_all.py

Note: plot_all.py will create all figures if the forecasts and data are available. Follow the instructions below to recreate individual figures.

Once completed, the figures can be found in the figures directory in the top-level directory and results in the results directory. These can be compared against the expected results that are found in the expected_results directory.

Generate individual figures

Individual scripts can be run by replacing plot_all.py above with the name of the script you would like to run (e.g., plot_figure2.py). If you only downloaded the 'lightweight' version from Zenodo, you will be unable to run plot_figure3.py or plot_figure6.py.

If data are already downloaded, figures can be run individually by starting the Docker image and executing scripts manually. Here is an example to recreate Fig. 2 from the manuscript. The commands should be issued from the scripts directory.

python plot_figure2.py

Code description

The top-level directory contains a few helpful scripts for working with the Docker environment. Descriptions of the files in the top-level directory are as follows (the .sh and .bat scripts provide the same functionality on different operating systems):

  • build_docker.sh: rebuilds the Docker image for this environment
  • download_data.sh: downloads and verifies checksums of the data from Zenodo
  • start_docker.sh: starts Docker container and provides command-line interface with pycsep environment active
  • run_docker.sh: runs the Docker container and automatically launches package
  • entrypoint.sh: entrypoint for the runnable Docker container

The code to execute the main experiment can be found in the scripts directory of this repository. The files are named according to the figure they create in the manuscript. The script plot_all.py will generate all of the figures. Descriptions of the files in the scripts directory are as follows:

  • plot_all.py: generates all figures listed below
  • plot_figure2.py: plots RELM and Italian time-independent forecasts with the catalog used to evaluate the forecasts
  • plot_figure3.py: plots selected catalogs from UCERF3-ETAS forecast
  • plot_figure4.py: plots S-test and N-test evaluations for RELM and Italian time-independent forecasts
  • plot_figure5.py: plots t-test and W-test evaluations for RELM and Italian time-independent forecasts
  • plot_figure6.py: plots S-test and N-test evaluations for UCERF3-ETAS forecasts
  • plot_figure7.py: illustrates plotting capabilities and manipulation of gridded forecasts
  • experiment_utilities.py: functions and configuration needed to run the above scripts
  • download_data.py: downloads data from Zenodo (see DOI link at the top)

Software versions

  • python>=3.7
  • pycsep=0.5.2

To obtain the environment used for publishing this manuscript use Docker. Advanced users can recreate the environment using conda running on Ubuntu 20.04 LTS.

Computational effort

On a recent (2021) laptop with a 4.6GHz Intel i7, the total runtimes on Windows were as follows:

  • 'lightweight' version in Docker and native OS: ~2min
  • 'full' version:
    • in Docker: ~1h 40min (~3h on a late 2017 MacBook Pro with a 2.9GHz Intel i7)
    • in native OS: ~1h 5min

The Docker environment introduces latency with I/O operations (only noticible when reading the catalog-based UCERF3-ETAS forecast file, and in some computing environments).

References

Krafczyk, M. S., Shi, A., Bhaskar, A., Marinov, D., and Stodden, V. (2021). Learning from reproducing computational results: introducing three principles and the reproduction package. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2197). doi: 10.1098/rsta.2020.0069