Skip to main content

pelicun

Probabilistic Estimation of Losses, Injuries, and Community resilience Under Natural hazard events

Latest Release Tests codecov Ruff License

What is it?

pelicun is a Python package that provides tools for assessment of damage and losses due to natural hazard events. It uses a stochastic damage and loss model that is an extension of the high-resolution PEER performance assessment methodology described in FEMA P58 (FEMA, 2012). While FEMA P58 aims to assess the seismic performance of a building, with pelicun we provide a more versatile, hazard-agnostic tool to assess the performance of several types of assets in the built environment.

pelicun ships with the comprehensive Damage and Loss Model Library (DLML), distributed as the simcenter-dlml package and installed automatically together with pelicun. The bundled models are available immediately after installation—no separate download step is needed and no network access is required at runtime.

Detailed documentation of the available methods and their use is available at http://nheri-simcenter.github.io/pelicun

What can I use it for?

pelicun quantifies losses from an earthquake or hurricane scenario in the form of decision variables. This functionality is typically utilized for performance-based engineering and regional risk assessment. There are several steps of performance assessment that pelicun can help with:

  • Describe the joint distribution of asset response. The response of a structure or other type of asset to an earthquake or hurricane wind is typically described by so-called engineering demand parameters (EDPs). pelicun provides methods that take a finite number of EDP vectors and find a multivariate distribution that describes the joint distribution of EDP data well. You can control the type of target distribution, apply truncation limits and censor part of the data to consider detection limits in your analysis. Alternatively, you can choose to use your EDP vectors as-is without resampling from a fitted distribution.

  • Define the damage and loss model of a building. The component damage and loss data from the first two editions of FEMA P58 and the HAZUS earthquake and hurricane models for buildings are provided with pelicun. This makes it easy to define building components without having to collect and provide all the data manually. The stochastic damage and loss model is designed to facilitate modeling correlations between several parameters of the damage and loss model.

  • Estimate component damages. Given a damage and loss model and the joint distribution of EDPs, pelicun provides methods to estimate the amount of damaged components and the number of cases with collapse.

  • Estimate consequences. Using information about collapse and component damages, the following consequences can be estimated with the loss model: reconstruction cost and time, unsafe placarding (red tag), injuries and fatalities.

Why should I use it?

  1. It is free and it always will be.
  2. It is open source. You can always see what is happening under the hood.
  3. It is efficient. The loss assessment calculations in pelicun use numpy, scipy, and pandas libraries to efficiently propagate uncertainties and provide detailed results quickly.
  4. You can trust it. Every function in pelicun is tested after every commit through the continuous integration workflow on GitHub Actions, which runs the full test suite on Linux, Windows, and macOS across all supported Python versions.
  5. You can extend it. If you have other methods that you consider better than the ones we already offer, we encourage you to fork the repo, and extend pelicun with your approach. You do not need to share your extended version with the community, but if you are interested in doing so, contact us and we are more than happy to merge your version with the official release.

Installation

pelicun is available at the Python Package Index (PyPI). You can simply install it using pip as follows:

pip install pelicun

If you are interested in using an earlier version, you can install it with the following command:

pip install pelicun==2.6.0

Note that 2.6.0 is the last minor version before the v3.0 release. Other earlier versions can be found here.

Documentation and usage examples

The documentation for pelicun can be accessed here. It includes information for users, instructions for developers and usage examples.

Changelog

Detailed release notes for all versions are available in CHANGELOG.md and in the online documentation.

License

pelicun is distributed under the BSD 3-Clause license, see LICENSE.

Acknowledgment

This material is based upon work supported by the National Science Foundation under Grants No. 1612843 2131111. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Contact

Adam Zsarnóczay, NHERI SimCenter, Stanford University, adamzs@stanford.edu

Metadata

Release files for pelicun 3.10.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pelicun 3.10.0
File Size Uploaded
pelicun-3.10.0.tar.gz 316.6 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for pelicun 3.10.0
File Interpreter ABI Platform
pelicun-3.10.0-py3-none-any.whl Python 3 none any Details

Total release size: 732.1 kB

Release files / pelicun-3.10.0.tar.gz

Download URL pelicun-3.10.0.tar.gz
Size 316.6 kB
Tags Source
SHA-256 checksum
How to use checksums
8521eee2c2ea0eeafc9ffdc7d05db0364546561c4391163680ce713b3fa9ad07
BLAKE2b-256 checksum
How to use checksums
9c5156c112532dc74f77396691f3220ee1892e7c54c0c08d0d4060a1b4bee47a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 5, 2026.

Transparency log

Release files / pelicun-3.10.0-py3-none-any.whl

Download URL pelicun-3.10.0-py3-none-any.whl
Size 415.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
d2e56b845afa6b0a4b14ad7ca6cf686bc151c2a7ac7ed887fd78edaec25f0b8f
BLAKE2b-256 checksum
How to use checksums
35e2f35527d6b6a1b016bd2e0cda0b2e15a02f15717c584e4a98231adf76e6c3
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 5, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

3.10.0 This release

2 release files

3.9.0

2 release files

3.8.1

2 release files

3.8.0

2 release files

3.7.1

2 release files

3.7.0

2 release files

3.6.1

2 release files

3.6

2 release files

3.5.1

2 release files

3.5.0

2 release files

3.4.0

2 release files

3.3.5

2 release files

3.3.4

2 release files

3.3.3

2 release files

3.3.2

2 release files

3.3.1

2 release files

3.3

2 release files

3.2

2 release files

3.1

2 release files

2.6.0

2 release files

2.5.0

2 release files

2.1.1

2 release files

2.1.0

2 release files

2.0.9

2 release files

2.0.1

2 release files

2.0

2 release files

1.1.6

3 release files

1.1.0

3 release files

1.0.5

3 release files

1.0.0

3 release files

0.9.1

3 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page