OasisLMF Package

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Introduction

The oasislmf Python package, loosely called the model development kit (MDK) or the MDK package, provides a command line toolkit for developing, testing and running Oasis models end-to-end locally, or remotely via the Oasis API. It can generate ground-up losses (GUL), direct/insured losses (IL) and reinsurance losses (RIL). It can also generate deterministic losses at all these levels.


Features

For running models locally the CLI provides a model subcommand with the following options:

  • model generate-exposure-pre-analysis: generate new Exposure input using user custom code (e.g. geo-coding, exposure enhancement, or disaggregation).

  • model generate-keys: generates Oasis keys files from model lookups; these are essentially line items of (location ID, peril ID, coverage type ID, area peril ID, vulnerability ID) where peril ID and coverage type ID span the full set of perils and coverage types that the model supports; if the lookup is for a complex/custom model the keys file will have the same format except that area peril ID and vulnerability ID are replaced by a model data JSON string. Keys can be output in oasis, json, or parquet format via the --keys-format flag.

  • model generate-oasis-files: generates the Oasis input files for losses (GUL, GUL + IL, or GUL + IL + RIL); it requires the provision of source exposure and optionally source accounts and reinsurance info and scope files (in OED format), as well as assets for instantiating model lookups and generating keys files.

  • model generate-pre-loss: runs pre-loss hooks before the main loss calculation. Custom code can be injected via --pre-loss-module / --pre-loss-class-name.

  • model generate-post-file-gen: runs post-file-generation hooks after Oasis input files are created but before losses are computed. Custom code injected via --post-file-gen-module / --post-file-gen-class-name.

  • model generate-losses: generates losses (GUL, or GUL + IL, or GUL + IL + RIL) from a set of pre-existing Oasis files.

  • model generate-losses-chunk: generates losses for a single chunk (used internally by the platform worker).

  • model generate-losses-output: post-processes and collects output from chunked loss generation.

  • model run: runs the model from start to finish — exposure pre-analysis → keys → Oasis files → losses — from the source OED exposure, and optionally source accounts and reinsurance info and scope files.

  • model run-postanalysis: runs the post-analysis hook on a completed set of results without re-running the full model.

  • model generate-doc: prints the analysis settings JSON schema documentation.

  • model generate-computation-settings-json-schema: outputs the computation settings JSON schema for tooling.


The optional --summarise-exposure flag can be issued with model generate-oasis-files and model run to generate a summary of Total Insured Values (TIVs) grouped by coverage type and peril. This produces the exposure_summary_report.json file.

For remote model execution the api subcommand provides the following subcommands:

  • api run: runs the model remotely (same as model run) but via the Oasis API

  • api generate-oasis-files: remotely generates Oasis input files via the API

  • api generate-losses: remotely generates losses via the API

  • api list: lists analyses available on the remote API server

  • api get: retrieves results from a remote analysis

  • api delete: deletes a remote analysis

For generating deterministic losses an exposure run subcommand is available:

  • exposure run: generates deterministic losses (GUL, or GUL + IL, or GUL + IL + RIL)

For utility and maintenance:

  • warmup: pre-compiles all Numba JIT functions to eliminate cold-start overhead on the first model run. Recommended after installation — especially in Docker images — to avoid a 2–6 minute compilation delay on first use.

  • config: describes the format of the MDK configuration JSON file.

  • config update: updates a config JSON file with new values.

  • version: prints the installed oasislmf version.

  • admin enable-bash-complete: activates bash tab-completion (see Enable Bash completion).

  • test: runs a regression test against an expected set of outputs.


The reusable libraries are organised into several sub-packages, the most relevant of which from a model developer or user’s perspective are:

  • api_client

  • model_preparation

  • model_execution

  • utils


Minimum Python Requirements

Starting from 1st January 2019, Pandas will no longer be supporting Python 2. As Pandas is a key dependency of the MDK we are dropping Python 2 (2.7) support as of this release (1.3.4). The last version which still supports Python 2.7 is version 1.3.3 (published 12/03/2019).

Also for this release (and all future releases) a minimum of Python 3.10 is required.


Installation

The latest released version of the package, or a specific package version, can be installed using pip:

pip install oasislmf
pip install oasislmf==<version string>

Alternatively you can install the latest development version using:

pip install git+{https,ssh}://git@github.com/OasisLMF/OasisLMF

You can also install from a specific branch <branch name> using:

pip install [-v] git+{https,ssh}://git@github.com/OasisLMF/OasisLMF.git@<branch name>#egg=oasislmf

macOS Apple Silicon (M1/M2/M3/M4)

OasisLMF installs natively on Apple Silicon Macs via pip install oasislmf. Ensure you have:

  • Python 3.10+ — the system Python on macOS is 3.9; install a newer version via brew install python@3.12 or pyenv.

  • macOS 12 (Monterey) or later — required for scipy ARM64 wheels.

For optional geospatial extras (pip install oasislmf[extra]), also install:

brew install spatialindex geos

JIT Cache Warmup

OasisLMF uses Numba JIT compilation for performance-critical calculations. The first run after installation incurs a one-time compilation overhead of 2–6 minutes. Pre-compile all JIT functions to eliminate this delay:

oasislmf warmup

In Docker images, you can bake the cache in at build time:

RUN pip install oasislmf && oasislmf warmup

Note

JIT caches are CPU-architecture-specific. oasislmf warmup in a Docker image is most effective when the build machine and the runtime machine share the same CPU architecture.


Enable Bash completion

Bash completion is a functionality which bash helps users type their commands by presenting possible options when users press the tab key while typing a command.

Once oasislmf is installed you’ll need to be activate the feature by sourcing a bash file (only needs to be run once).


Local

oasislmf admin enable-bash-complete

Global

echo 'complete -C completer_oasislmf oasislmf' | sudo tee /usr/share/bash-completion/completions/oasislmf

Dependencies

System

The package provides a built-in lookup framework (oasislmf.model_preparation.lookup.OasisLookup) which uses the Rtree Python package, which in turn requires the libspatialindex spatial indexing C library.

https://libspatialindex.github.io/index.html


Linux users can install the development version of libspatialindex from the command line using apt.

[sudo] apt install -y libspatialindex-dev

and OS X users can do the same via brew.

brew install spatialindex

The PiWind demonstration model uses the built-in lookup framework, therefore running PiWind or any model which uses the built-in lookup, requires that you install libspatialindex.


GNU/Linux

For GNU/Linux the following is a specific list of required system libraries

  • Debian: g++ compiler build-essential, libtool, zlib1g-dev autoconf on debian distros

sudo apt install g++ build-essential libtool zlib1g-dev autoconf

  • Red Hat: ‘Development Tools’ and zlib-devel


Python

Package Python dependencies are controlled by pip-tools. To install the development dependencies first, install pip-tools using:

pip install pip-tools

and run:

pip-sync

To add new dependencies to the development requirements add the package name to requirements.in or to add a new dependency to the installed package add the package name to requirements-package.in. Version specifiers can be supplied to the packages but these should be kept as loose as possible so thatall packages can be easily updated and there will be fewer conflict when installing.


After adding packages to either *.in file:

pip-compile && pip-sync

This should be ran ensuring the development dependencies are kept up to date.


ods_tools

OasisLMF uses the ods_tools package to read exposure files and the settings files. The compatible version for each OasisLMF release is managed in the requirements files. Below is the current summary:

  • OasisLMF 1.23.x or before => no ods_tools

  • OasisLMF 1.26.x => use ods_tools 2.3.2

  • OasisLMF 1.27.0 => use ods_tools 3.0.0 or later

  • OasisLMF 1.27.1 => use ods_tools 3.0.0 or later

  • OasisLMF 1.27.2 => use ods_tools 3.0.4 or later

  • OasisLMF 2.3.x => use ods_tools 3.2.x or later

  • OasisLMF 2.4.x => use ods_tools 4.0.x or later

  • OasisLMF 2.5.x => use ods_tools 5.0.x or later


pandas

Pandas has released its major version number 2 breaking some of the compatibility with the 1st version. Therefore, for all version of OasisLMF <= 1.27.2, the latest supported version for pandas is 1.5.3. Support for pandas 2, starts from version 1.27.3.


Testing

To test the code style run:

flake8

To test against all supported python versions run:

tox

To test against your currently installed version of python run:

py.test

To run the full test suite run:

./runtests.sh

Publishing

Version management and PyPI releases are handled automatically by the CI pipeline (version.yml and publish.yml workflows). Manually publishing is not normally required.

To build and upload manually (using modern build + twine):

pip install build twine
python -m build
twine upload dist/*

The __version__ value in oasislmf/__init__.py must be incremented before building.