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.
1.15.xstable/1.15.x From Feb 20211.23.xstable/1.23.x From Dec 20211.26.xstable/1.26.x From Jun 20221.27.xstable/1.27.x From Jan 20231.28.x(Up comming) July 2023
Until end of 2023
Until the year 2023, we will be following a six-month release cycle for our stable versions. During each six-month period, we will release a new stable version with added features. These updates will adhere to the Semantic Versioning (semver) format and will increment the minor version number.
That version of oaisislmf is then 'frozen' into a branch matching the new version number, so on release 1.28.0 the code base is copied to a branch stable/1.28.x where backported features and fixes are applied.
After 2023 Starting from 2023, we will transition to a yearly release cycle for our stable versions. Each year, we will release a new stable version with additional features.
Every month, we will provide updates to the latest stable version. These updates will include new compatible features and bug fixes, ensuring that our software remains up-to-date and reliable.
During the monthly update, if any bug fixes are required, they will also be applied to the older stable versions. This approach guarantees that all stable versions receive necessary bug fixes, while maintaining a consistent output numbers for that stable version.
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 (ex: geo-coding, exposure enhancement, or dis-aggregation...)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 stringmodel generate-oasis-files: generates the Oasis input CSV 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 filesmodel generate-losses: generates losses (GUL, or GUL + IL, or GUL + IL + RIL) from a set of pre-existing Oasis filesmodel run: runs the model from start to finish by generating losses (GUL, or GUL + IL, or GUL + IL + RIL) from the source exposure, and optionally source accounts and reinsurance info. and scope files (in OED or RMS format), as well as assets related to lookup instantiation and keys file generation
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 main subcommand:
api run: runs the model remotely (same asmodel run) but via the Oasis API
For generating deterministic losses an exposure run subcommand is available:
exposure run: generates deterministic losses (GUL, or GUL + IL, or GUL + IL + RIL)
The reusable libraries are organised into several sub-packages, the most relevant of which from a model developer or user's perspective are:
api_clientmodel_preparationmodel_executionutils
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 3.8 is required.
The latest released version of the package, or a specific package version, can be installed using pip:
pip install oasislmf[==<version string>]
Alternatively you can install the latest development version using:
pip install git+{https,ssh}://[email protected]/OasisLMF/OasisLMF
You can also install from a specific branch <branch name> using:
pip install [-v] git+{https,ssh}://[email protected]/OasisLMF/OasisLMF.git@<branch name>#egg=oasislmf
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)
oasislmf admin enable-bash-complete
echo 'complete -C completer_oasislmf oasislmf' | sudo tee /usr/share/bash-completion/completions/oasislmf
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.
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
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 that
all packages can be easily updated and there will be fewer conflict when installing.
After adding packages to either *.in file:
pip-compile && pip-sync
should be ran ensuring the development dependencies are kept up to date.
OasisLMF uses the ods_tools package to read exposure files and the setting files The version compatible with each OasisLMF is manage in the requirement files. below is the 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
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
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
Before publishing the latest version of the package make you sure increment the __version__ value in oasislmf/__init__.py, and commit the change. You'll also need to install the twine Python package which setuptools uses for publishing packages on PyPI. If publishing wheels then you'll also need to install the wheel Python package.
The distribution format can be either a source distribution or a platform-specific wheel. To publish the source distribution package run:
python setup.py publish --sdist
or to publish the platform specific wheel run:
python setup.py publish --wheel
To create a distribution for a non-host platform use the --plat-name flag:
python setup.py bdist_wheel --plat-name Linux_x86_64
or
python setup.py bdist_wheel --plat-name Darwin_x86_64
The first step is to create the distribution package with the desired format: for the source distribution run:
python setup.py sdist
which will create a .tar.gz file in the dist subfolder, or for the platform specific wheel run:
python setup.py bdist_wheel
which will create .whl file in the dist subfolder. To attach a GPG signature using your default private key you can then run:
gpg --detach-sign -a dist/<package file name>.{tar.gz,whl}
This will create .asc signature file named <package file name>.{tar.gz,whl}.asc in dist. You can just publish the package with the signature using:
twine upload dist/<package file name>.{tar.gz,whl} dist/<package file name>.{tar.gz,whl}.asc
The code in this project is licensed under BSD 3-clause license.
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