pelicun - v3.8.1

Changed

Enhanced DLML Test Suite: A complete overhaul of the test suite for the DLML module to improve reliability and test coverage:

• Replaced brittle, unit-style tests with a new suite of robust integration and end-to-end tests.
• Added a new end-to-end test using a local HTTP server (pytest-httpserver) to simulate the entire download workflow, ensuring high confidence in the process.
• Overhauled network mocks to be more realistic, allowing for more accurate validation of download behavior.

Improved DLML Module Configuration:

• Refactored the module to resolve all configuration from environment variables at runtime, significantly improving testability and isolation.

DLML Data:

• Updated the default data version for the Damage and Loss Model Library to v2.1.0.

Dependencies:

• Added pytest-httpserver as a new development dependency to support the enhanced end-to-end testing.

Fixed

DLML Robustness and Reliability:

• Increased network timeouts for file downloads to prevent errors on slower connections.
• Improved the reliability of the recommended dlml update command shown to users, ensuring it works correctly on all platforms, especially those with spaces in the Python path.

Developer Experience:

• Performed a comprehensive code quality pass on the entire test suite, standardizing docstrings, adding complete type hints, and improving code style for better maintainability.
• Made the main CLI script directly executable to simplify development and debugging workflows.

Climate Change - Natural Hazard and Storms - Python
Published by zsarnoczay 9 months ago

pelicun - v3.8

This release introduces a major new feature with the integrated Damage and Loss Model Library (DLML) module, modernizes the CI/CD pipeline, and enhances the testing infrastructure. The changes provide users with seamless access to the model library while maintaining backward compatibility.

Added

• DLML (Damage and Loss Model Library) Module: A new comprehensive module for efficient management of the Damage and Loss Model Library has been added to Pelicun:
  • Automatic Data Initialization: DLML data is now automatically initialized on first import, providing seamless access to model library.
  • CLI Integration: The DLML module is integrated with Pelicun's command-line interface for easy management
  • Comprehensive Testing: Added extensive unit tests and integration tests for the DLML functionality
• Enhanced Test Infrastructure: Significant improvements to the testing framework:
  • Network Isolation: Added network isolation capabilities for more robust testing
  • Improved Test Configuration: Enhanced test configuration management with better fixtures and utilities

Changed

• CI/CD Pipeline Modernization: Major update to the continuous integration and deployment system:
  • Unified CI Workflow: Consolidated multiple separate workflow files into a single comprehensive CI pipeline
  • GitHub API Integration: Implemented robust GitHub API authentication for better repository management
• Dependencies: Added requests as a project dependency to support new DLML functionality

Removed

• DLML Submodule: Removed the DLML Git submodule in favor of the new integrated DLML module approach
• Legacy CI Workflows: Removed several workflow files that were consolidated into the new unified CI pipeline:
  • deploy_to_pypi.yaml
  • docs_check.yaml
  • format_check.yml
  • lint.yml
  • spell_check.yml
  • tests.yml

Fixed

• Code Quality: Applied comprehensive fixes for code quality improvements throughout the codebase
• CI Workflow Issues: Resolved various issues in the CI workflow configuration for more reliable builds and testing

Climate Change - Natural Hazard and Storms - Python
Published by zsarnoczay 9 months ago

pelicun - v3.7.1

This is a patch release that enables flood loss simulation through SimCenter's backend and addresses a few bugs.

Climate Change - Natural Hazard and Storms - Python
Published by zsarnoczay 10 months ago

pelicun - v3.7

Version 3.7.0 (August 7, 2025)

Added

Regional Simulation Tool: A new regional simulation capability has been added to Pelicun as a temporary solution while the integrated feature is being developed. The tool currently supports Hazus EQ simulation using lifeline facility fragilities and includes:

• Batch Processing: Memory-efficient sequential and parallel batch processing using joblib for handling large-scale regional simulations
• Progress Reporting: Time and progress information output to track simulation status
• Command-line Interface: A new CLI system with pelicun as an installable command and regional_sim as the first available subcommand

Installable CLI: Pelicun now provides a modern command-line interface through a console_scripts entry point. The new pelicun/cli.py module serves as a subcommand dispatcher, making the tool easier to install and use from the command line.

Case-insensitive Unit Handling: Implemented case-insensitive unit label handling in input data processing, improving robustness when processing data with varying unit label capitalization.

Changed

Modernized Packaging: The project has been modernized to align with current Python packaging standards (PEP 621):

• All static package metadata (version, dependencies, etc.) has been migrated from setup.py to pyproject.toml
• pyproject.toml is now the single source of truth for package configuration
• Version number source has been moved to pyproject.toml
• setup.py file is now minimal, reserved for future dynamic build logic

Removed

Deprecated Tools: Removed HDF_to_CSV from tools as it is no longer used.

Fixed

Pandas Deprecation Warning: Fixed a minor issue in loss_model to avoid pandas deprecation warnings, ensuring compatibility with newer pandas versions.

R2D Configuration Handling: Applied bugfixes to handle advanced configurations from R2D.

Documentation Build Issues: Resolved various Sphinx warnings and build errors to ensure clean documentation generation.

Climate Change - Natural Hazard and Storms - Python
Published by zsarnoczay 10 months ago

pelicun - v3.6.1

Version 3.6.1 (May 16, 2025)

This is a patch release that addresses a bug in the Damage and Loss Model Library configuration files.

Changed

Damage and Loss Model Library: Updated the DLML submodule to the latest version to incorporate bug fixes and ensure users have access to properly functioning configuration files for all supported assessment methodologies.

Climate Change - Natural Hazard and Storms - Python
Published by zsarnoczay about 1 year ago

pelicun - v3.6

Climate Change - Natural Hazard and Storms - Python
Published by zsarnoczay about 1 year ago

pelicun - v3.5.1

Minor update to bring in the newly added Hazus v6.1 EQ building library from the DLML.

Climate Change - Natural Hazard and Storms - Python
Published by zsarnoczay over 1 year ago

pelicun - v3.5

Climate Change - Natural Hazard and Storms - Python
Published by zsarnoczay over 1 year ago

pelicun - v3.4

Added

Documentation pages: Documentation for pelicun 3 is back online. The documentation includes guides for users and developers as well as an auto-generated API reference. A lineup of examples is planned to be part of the documentation, highlighting specific features, including the new ones listed in this section.

Consequence scaling: This feature can be used to apply scaling factors to consequence and loss functions for specific decision variables, component types, locations and directions. This can make it easier to examine several different consequence scaling schemes without the need to repeat all calculations or write extensive custom code.

Capacity scaling: This feature can be used to modify the median of normal or lognormal fragility functions of specific components. Medians can be scaled by a factor or shifted by adding or subtracting a value.
This can make it easier to use fragility functions that are a function of specific asset features.

Loss functions: Loss functions are used to estimate losses directly from the demands. The damage and loss models were substantially restructured to facilitate the use of loss functions.

Loss combinations: Loss combinations allow for the combination of two types of losses using a multi-dimensional lookup table. For example, independently calculated losses from wind and flood can be combined to produce a single loss estimate considering both demands.

Utility demand: Utility demands are compound demands calculated using a mathematical expression involving other demands. Practical examples include the application of a mathematical expression on a demand before using it to estimate damage, or combining multiple demands with a multivariate expression to generate a combined demand.Such utility demands can be used to implement those multidimensional fragility models that utilize a single, one-dimensional distribution that is defined through a combination of multiple input variables.

Normal distribution with standard deviation: Added two new variants of "normal" in uq.py: normal_COV and normal_STD. Since the variance of the default normal random variables is currently defined via the coefficient of variation, the new normal_STD is required to define a normal random variable with zero mean. normal_COV is treated the same way as the default normal.

Weibull random variable: Added a Weibull random variable class in uq.py.

New DL_calculation.py input file options: We expanded configuration options in the DL_calculation.py input file specification. Specifically, we added CustomDLDataFolder for specifying additional user-defined components.

Warnings in red: Added support for colored outputs. In execution environments that support colored outputs, warnings are now shown in red.

Code base related additions, which are not directly implementing new features but are nonetheless enhancing robustness, include the following:

• pelicun-specific warnings with the option to disable them
• a JSON schema for the input file used to configure simulations through DL_calculation.py
• addition of type hints in the entire code base
• addition of slots in all classes, preventing on-the-fly definition of new attributes which is prone to bugs

Changed

• Updated random variable class names in uq.py.
• Extensive code refactoring for improved organization and to support the new features. We made a good-faith effort to maintain backwards compatibility, and issue helpful warnings to assist migration to the new syntax.
• Moved most of the code in DL_calculation.py to assessment.py and created an assessment class.
• Migrated to Ruff for linting and code formatting. Began using mypy for type checking and codespell for spell checking.

Deprecated

• .bldg_repair attribute was renamed to .loss
• .repair had also been used in the past, please use .loss instead.
• In the damage and loss model library, fragility_DB was renamed to damage_DB and bldg_repair_DB was renamed to loss_repair_DB.
• load_damage_model was renamed to load_model_parameters and the syntax has changed. Please see the applicable warning message when using load_damage_model for the updated syntax.
• {damage model}.sample was deprecated in favor of {damage model}.ds_model.sample.
• The DMG- flag in the loss_map index is no longer required.
• BldgRepair column is deprecated in favor of Repair.
• load_model -> load_model_parameters
• {loss model}.save_sample -> '{loss model}.ds_model.save_sample. The same applies to load_sample.

Removed

• No features were removed in this version.
• We suspended the use of flake8 and pylint after adopting the use of ruff.

Fixed

• Fixed a bug affecting the random variable classes, where the anchor random variable was not being correctly set.
• Enforced a value of 1.0 for non-directional multipliers for HAZUS analyses.
• Fixed bug in demand cloning: Previously demand unit data were being left unmodified during demand cloning operations, leading to missing values.
• Reviewed and improved docstrings in the entire code base.

Climate Change - Natural Hazard and Storms - Python
Published by zsarnoczay over 1 year ago

pelicun - v3.3

• Changes affecting backwards compatibility

  • Remove "bldg" from repair consequence output filenames: The increasing scope of Pelicun now covers simulations for transportation and water networks. Hence, labeling repair consequence outputs as if they were limited to buildings no longer seems appropriate. The bldg label was dropped from the following files: DV_bldg_repair_sample,DV_bldg_repair_stats,DV_bldg_repair_grp, DV_bldg_repair_grp_stats, DV_bldg_repair_agg, DV_bldg_repair_agg_stats.

• Deprecation warnings

  • Remove Bldg from repair settings label in DL configuration file: Following the changes above, we dropped Bldg from BldgRepair when defining settings for repair consequence simulation in a configuration file. The previous version (i.e., BldgRepair) will keep working until the next major release, but we encourage everyone to adopt the new approach and simply use the Repair keyword there.

• New features

  • Location-specific damage processes: This new feature is useful when you want damage to a component type to induce damage in another component type at the same location only. For example, damaged water pipes on a specific story can trigger damage in floor covering only on that specific story. Location-matching is performed automatically without you having to define component pairs for every location using the following syntax: '1_CMP.A-LOC', {'DS1': 'CMP.B_DS1'} , where DS1 of CMP.A at each location triggers DS1 of CMP.B at the same location.

  • New custom_model_dir argument for DL_calculation: This argument allows users to prepare custom damage and loss model files in a folder and pass the path to that folder to an auto-population script through DL_calculation. Within the auto-population script, they can reference only the name of the files in that folder. This provides portability for simulations that use custom models and auto population, such as some of the advanced regional simualtions in SimCenter's R2D Tool.

  • Extend Hazus EQ auto population sripts to include water networks: Automatically recognize water network assets and map them to archetypes from the Hazus Earthquake technical manual.

  • Introduce convert_units function: Provide streamlined unit conversion using the pre-defined library of units in Pelicun. Allows you to convert a variable from one unit to another using a single line of simple code, such as
    converted_height = pelicun.base.convert_units(raw_height, unit='m', to_unit='ft')
    While not as powerful as some of the Python packages dedicated to unit conversion (e.g., Pint), we believe the convenience this function provides for commonly used units justifies its use in several cases.

• Architectural and code updates

  • Split model.py into subcomponents: The model.py file was too large and its contents were easy to refactor into separate modules. Each model type has its own python file now and they are stored under the model folder.

  • Split the RandomVariable class into specific classes: It seems more straightforward to grow the list of supported random variables by having a specific class for each kind of RV. We split the existing large RandomVariable class in uq.py leveraging inheritance to minimize redundant code.

  • Automatic code formatting: Further improve consistency in coding style by using black to review and format the code when needed.

  • Remove bldg from variable and class names: Following the changes mentioned earlier, we dropped bldg from lables where the functionality is no longer limited to buildings.

  • Introduce calibrated attribute for demand model: This new attribute will allow users to check if a model has already been calibrated to the provided empirical data.

  • Several other minor improvements; see commit messages for details.

• Dependencies

  • Ceiling raised for pandas, supporting version 2.0 and above up until 3.0.

Climate Change - Natural Hazard and Storms - Python
Published by zsarnoczay about 2 years ago

pelicun - v3.2

• Changes that might affect backwards compatibility:

  • Unit information is included in every output file. If you parse Pelicun outputs and did not anticipate a Unit entry, your parser might need an update.

  • Decision variable types in the repair consequence outputs are named using CamelCase rather than all capitals to be consistent with other parts of the codebase. For example, we use "Cost" instead of "COST". This might affect post-processing scripts.

  • For clarity, "ea" units were replaced with "unitless" where appropriate. There should be no practical difference between the calculations due to this change. Interstory drift ratio demand types are one example.

  • Weighted component block assignment is no longer supported. We recommend using more versatile multiple component definitions (see new feature below) to achieve the same effect.

  • Damage functions (i.e., assign quantity of damage as a function of demand) are no longer supported. We recommend using the new multilinear CDF feature to develop theoretically equivalent but more efficient models.

• New multilinear CDF Random Variable allows using the multilinear approximation of any CDF in the tool.

• Capacity adjustment allows adjusting (scaling or shifting) default capacities (i.e., fragility curves) with factors specific to each Performance Group.

• Support for multiple definitions of the same component at the same location-direction. This feature facilitates adding components with different block sizes to the same floor or defining multiple tenants on the same floor, each with their own set of components.

• Support for cloning demands, that is, taking a provided demand dataset, creating a copy and considering it as another demand. For example, you can provide results of seismic response in the X direction and automatically prepare a copy of them to represent results in the Y direction.

• Added a comprehensive suite of more than 140 unit tests that cover more than 93% of the codebase. Tests are automatically executed after every commit using GitHub Actions and coverage is monitored through Codecov.io. Badges at the top of the Readme show the status of tests and coverage. We hope this continuous integration facilitates editing and extending the existing codebase for interested members of the community.

• Completed a review of the entire codebase using flake8 and pylint to ensure PEP8 compliance. The corresponding changes yielded code that is easier to read and use. See guidance in Readme on linting and how to ensure newly added code is compliant.

• Models for estimating Environmental Impact (i.e., embodied carbon and energy) of earthquake damage as per FEMA P-58 are included in the DL Model Library and available in this release.

• "ListAllDamageStates" option allows you to print a comprehensive list of all possible damage states for all components in the columns of the DMG output file. This can make parsing the output easier but increases file size. By default, this option is turned off and only damage states that affect at least one block are printed.

• Damage and Loss Model Library

  • A collection of parameters and metadata for damage and loss models for performance based engineering. The library is available and updated regularly in the DB_DamageAndLoss GitHub Repository.

  • This and future releases of Pelicun have the latest version of the library at the time of their release bundled with them.

• DL_calculation tool

  • Support for combination of built-in and user-defined databases for damage and loss models.

  • Results are now also provided in standard SimCenter JSON format besides the existing CSV tables. You can specify the preferred format in the configuration file under Output/Format. The default file format is still CSV.

  • Support running calculations for only a subset of available consequence types.

• Several error and warning messages added to provide more meaningful information in the log file when something goes wrong in a simulation.

• Update dependencies to more recent versions.

• The online documentation is significantly out of date. While we are working on an update, we recommend using the documentation of the DL panel in SimCenter's PBE Tool as a resource.

Climate Change - Natural Hazard and Storms - Python
Published by zsarnoczay over 2 years ago

pelicun - v3.1

• Calculation settings are now assessment-specific. This allows you to use more than one assessments in an interactive calculation and each will have its own set of options, including log files.

• The uq module was decoupled from the others to enable standalone uq calculations that work without having an active assessment.

• A completely redesigned DL_calculation.py script that provides decoupled demand, damage, and loss assessment and more flexibility when setting up each of those when pelicun is used with a configuration file in a larger workflow.

• Two new examples that use the DL_calculation.py script and a json configuration file were added to the example folder.

• A new example that demonstrates a detailed interactive calculation in a Jupyter notebook was added to the following DesignSafe project: https://www.designsafe-ci.org/data/browser/public/designsafe.storage.published/PRJ-3411v5 This project will be extended with additional examples in the future.

• Unit conversion factors moved to an external file (settings/default_units) to make it easier to add new units to the list. This also allows redefining the internal units through a complete replacement of the factors. The internal units continue to follow the SI system.

• Substantial improvements in coding style using flake8 and pylint to monitor and help enforce PEP8.

• Several performance improvements made calculations more efficient, especially for large problems, such as regional assessements or tall buildings investigated using the FEMA P-58 methodology.

• Several bugfixes and a large number of minor changes that make the engine more robust and easier to use.

• Update recommended Python version to 3.10 and other dependencies to more recent versions.

Climate Change - Natural Hazard and Storms - Python
Published by zsarnoczay over 3 years ago

pelicun - v3.0

• The architecture was redesigned to better support interactive calculation and provide a low-level integration across all supported methods. This is the first release with the new architecture. Frequent updates are planned to provide additional examples, tests, and bugfixes in the next few months.

• New assessment module introduced to replace control module:

  • Provides a high-level access to models and their methods
  • Integrates all types of assessments into a uniform approach
  • Most of the methods from the earlier control module were moved to the model module

• Decoupled demand, damage, and loss calculations:

  • Fragility functions and consequence functions are stored in separate files. Added new methods to the db module to prepare the corresponding data files and re-generated such data for FEMA P58 and Hazus earthquake assessments. Hazus hurricane data will be added in a future release.
  • Decoupling removed a large amount of redundant data from supporting databases and made the use of HDF and json files for such data unnecessary. All data are stored in easy-to-read csv files.
  • Assessment workflows can include all three steps (i.e., demand, damage, and loss) or only one or two steps. For example, damage estimates from one analysis can drive loss calculations in another one.

• Integrated damage and loss calculation across all methods and components:

  • This includes phenomena such as collapse, including various collapse modes, and irreparable damage.
  • Cascading damages and other interdependencies between various components can be introduced using a damage process file.
  • Losses can be driven by damages or demands. The former supports the conventional damage->consequence function approach, while the latter supports the use of vulnerability functions. These can be combined within the same analysis, if needed.
  • The same loss component can be driven by multiple types of damages. For example, replacement can be triggered by either collapse or irreparable damage.

• Introduced Options in the configuration file and in the base module:

  • These options handle settings that concern pelicun behavior;
  • general preferences that might affect multiple assessment models;
  • and settings that users would not want to change frequently.
  • Default settings are provided in a default_config.json file. These can be overridden by providing any of the prescribed keys with a user-defined value assigned to them in the configuration file for an analysis.

• Introduced consistent handling of units. Each csv table has a standard column to describe units of the data in it. If the standard column is missing, the table is assumed to use SI units.

• Introduced consistent handling of pandas MultiIndex objects in headers and indexes. When tabular data is stored in csv files, MultiIndex objects are converted to simple indexes by concatenating the strings at each level and separating them with a -. This facilitates post-processing csv files in pandas without impeding post-processing those files in non-Python environments.

• Updated the DL_calculation script to support the new architecture. Currently, only the config file input is used. Other arguments were kept in the script for backwards compatibility; future updates will remove some of those arguments and introduce new ones.

• The log files were redesigned to provide more legible and easy-to-read information about the assessment.

Climate Change - Natural Hazard and Storms - Python
Published by zsarnoczay over 4 years ago

pelicun - v2.6

• Support EDPs with more than 3 characters and/or a variable in their name. For example, SA_1.0 or SA_T1
• Support fitting normal distribution to raw EDP data (lognormal was already available)
• Extract key settings to base.py to make them more accessible for users.
• Minor bug fixes mostly related to hurricane storm surge assessment

Climate Change - Natural Hazard and Storms - Python
Published by zsarnoczay almost 5 years ago

pelicun - pelicun v2.5

• Extend the uq module to support:
  • More efficient sampling, especially when most of the random variables in the model are either independent or perfectly correlated.
  • More accurate and more efficient fitting of multivariate probability distributions to raw EDP data.
  • Arbitrary marginals (beyond the basic Normal and Lognormal) for joint distributions.
  • Latin Hypercube Sampling
• Introduce external auto-population scripts and provide an example for hurricane assessments.
• Add a script to help users convert HDF files to CSV (HDF_to_CSV.py under tools)
• Use unique and standardized attribute names in the input files
• Migrate to the latest version of Python, numpy, scipy, and pandas (see setup.py for required minimum versions of those tools).
• Bug fixes and minor improvements to support user needs:
  • Add 1.2 scale factor for EDPs controlling non-directional Fragility Groups.
  • Remove dependency on scipy's truncnorm function to avoid long computation times due to a bug in recent scipy versions.

Climate Change - Natural Hazard and Storms - Python
Published by zsarnoczay over 5 years ago

pelicun - pelicun v2.1.1

• Aggregate DL data from JSON files to HDF5 files. This greatly reduces the number of files and makes it easier to share databases.
• Significant performance improvements in EDP fitting, damage and loss calculations, and output file saving.
• Add log file to pelicun that records every important calculation detail and warnings.
• Add 8 new EDP types: RID, PMD, SA, SV, SD, PGD, DWD, RDR.
• Drop support for Python 2.x and add support for Python 3.8.
• Extend auto-population logic with solutions for HAZUS EQ assessments.
• Several bug fixes and minor improvements to support user needs.

Climate Change - Natural Hazard and Storms - Python
Published by zsarnoczay almost 6 years ago

pelicun - pelicun v2.0.0

• migrated to the latest version of Python, numpy, scipy, and pandas
  see setup.py for required minimum versions of those tools
• Python 2.x is no longer supported
• improve DL input structure to
  = make it easier to define complex performance models
  = make input files easier to read
  = support custom, non-PACT units for component quantities
  = support different component quantities on every floor
• updated FEMA P58 DL data to use ea for equipment instead of units such as KV, CF, AP, TN
• add FEMA P58 2nd edition DL data
• support EDP inputs in standard csv format
• add a function that produces SimCenter DM and DV json output files
• add a differential evolution algorithm to the EDP fitting function to do a better job at finding the global optimum
• enhance DL_calculation.py to handle multi-stripe analysis (significant contributions by Joanna Zou):
  = recognize stripe_ID and occurrence rate in BIM/EVENT file
  = fit a collapse fragility function to empirical collapse probabilities
  = perform loss assessment for each stripe independently and produce corresponding outputs

v1.2

• support for HAZUS hurricane wind damage and loss assessment
• add HAZUS hurricane DL data for wooden houses
• move DL resources inside the pelicun folder so that they come with pelicun when it is pip installed
• add various options for EDP fitting and collapse probability estimation
• improved the way warning messages are printed to make them more useful

Climate Change - Natural Hazard and Storms - Python
Published by zsarnoczay over 6 years ago

pelicun - pelicun v1.1

Major new features:

• converted to a common JSON format for FEMA P58 and HAZUS Damage and Loss data
• added component-assembly-based (HAZUS-style) loss assessment methodology for earthquake

Climate Change - Natural Hazard and Storms - Python
Published by zsarnoczay over 7 years ago

pelicun - pelicun v1.0

Climate Change - Natural Hazard and Storms - Python
Published by zsarnoczay over 7 years ago