From 059613c1b66baa533475f2c376967582839ce4ab Mon Sep 17 00:00:00 2001 From: Joe Moorhouse <5102656+joemoorhouse@users.noreply.github.com> Date: Sun, 15 Sep 2024 18:56:42 +0100 Subject: [PATCH] Partial implementation of a vulnerability-config-based worked example using TUDelft flood data. (#349) * Generation of config via tests and tidy. Signed-off-by: Joe Moorhouse <5102656+joemoorhouse@users.noreply.github.com> * Generation of config via tests and tidy. Signed-off-by: Joe Moorhouse <5102656+joemoorhouse@users.noreply.github.com> * Simplify maintenance of requirements-docs.txt. Signed-off-by: Joe Moorhouse <5102656+joemoorhouse@users.noreply.github.com> * Tidy documentation; reorder for increased use of notebooks. Signed-off-by: Joe Moorhouse <5102656+joemoorhouse@users.noreply.github.com> * Lint and tidy. Signed-off-by: Joe Moorhouse <5102656+joemoorhouse@users.noreply.github.com> * Apply extra beta distribution check. Signed-off-by: Joe Moorhouse <5102656+joemoorhouse@users.noreply.github.com> --------- Signed-off-by: Joe Moorhouse <5102656+joemoorhouse@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- .gitignore | 2 + .pre-commit-config.yaml | 2 +- docs/Makefile | 4 +- .../asset_level_impacts.ipynb} | 0 .../hazard_indicators.ipynb} | 0 docs/getting-started.rst | 4 +- docs/index.rst | 2 +- docs/requirements-docs.txt | 237 +- docs/user-guide.rst | 63 +- docs/user-guide/introduction.ipynb | 64 + .../vulnerability_config.ipynb} | 1 - {docs => methodology}/toadapt/README.md | 0 .../toadapt/coastal_flood/damage_funs.png | Bin .../toadapt/coastal_flood/exposure2.png | Bin {docs => methodology}/toadapt/commands.rst | 0 .../toadapt/handbook/onboarding.rst | 0 .../onboarding/damage_with_uncertainty.pdf | Bin .../onboarding/damage_with_uncertainty.png | Bin .../handbook/onboarding/disruption_curve.pdf | Bin .../handbook/onboarding/disruption_curve.png | Bin .../handbook/onboarding/exceedance_curve.pdf | Bin .../handbook/onboarding/exceedance_curve.png | Bin .../onboarding/histo_from_exceedance.pdf | Bin .../onboarding/histo_from_exceedance.png | Bin .../handbook/onboarding/return_periods.pdf | Bin .../handbook/onboarding/return_periods.png | Bin .../onboarding/vulnerability_curve.pdf | Bin .../onboarding/vulnerability_curve.png | Bin .../vulnerability_with_uncertainty.pdf | Bin .../vulnerability_with_uncertainty.png | Bin {docs => methodology}/toadapt/hazards.rst | 0 .../toadapt/hazards/README.md | 0 .../toadapt/hazards/coastal_floods.rst | 0 .../toadapt/hazards/landslide.rst | 0 .../toadapt/hazards/references.bib | 0 .../toadapt/hazards/riverine_floods.rst | 0 .../toadapt/hazards/water_stress.rst | 0 .../toadapt/hazards/wildfire.rst | 0 .../toadapt/hazards/windstorm.rst | 0 .../toadapt/landslide/LSImap.PNG | Bin .../toadapt/landslide/land_cover.PNG | Bin .../landslide/landslide_classification.PNG | Bin .../toadapt/landslide/landslide_matrix.PNG | Bin .../toadapt/landslide/lithology.PNG | Bin .../toadapt/landslide/terrain.PNG | Bin .../toadapt/methodology/PRmodelling.png | Bin .../toadapt/methodology/ex_curve.png | Bin .../toadapt/methodology/flood_impact_JRC.png | Bin .../toadapt/methodology/histogram.png | Bin .../toadapt/methodology/intensity2.png | Bin .../damage_function_spain_riverflood.png | Bin .../toadapt/river_flood/damage_funs.png | Bin .../toadapt/river_flood/exposure2.png | Bin .../toadapt/river_flood/impact3.png | Bin .../toadapt/river_flood/intensity2.png | Bin .../river_flood/intensity2_riverflood.png | Bin .../toadapt/waterStress/waterstress1.PNG | Bin .../toadapt/waterStress/waterwithdrawal.PNG | Bin .../toadapt/wildfire/2. damage_function.png | Bin .../toadapt/wildfire/EAI_map.png | Bin .../toadapt/wildfire/damage_funs.png | Bin .../toadapt/wildfire/exposure2.png | Bin .../toadapt/wildfire/impact3.png | Bin .../toadapt/wildfire/intensity2.png | Bin .../toadapt/wildfire/powerplants.png | Bin .../toadapt/wildfire/wildfire_damage.png | Bin .../toadapt/wildfire/wildfire_impact.png | Bin .../toadapt/wildfire/wildfire_intensity.png | Bin .../toadapt/wildfire/wildfires_intensity2.png | Bin .../toadapt/windstorm/damage_funs_wind3.png | Bin .../toadapt/windstorm/exposure2.png | Bin .../toadapt/windstorm/wind_damage.png | Bin .../toadapt/windstorm/wind_impact_4TU.png | Bin .../toadapt/windstorm/wind_intensity5.png | Bin pdm.lock | 3959 +++++++++++++---- pyproject.toml | 29 +- src/physrisk/kernel/vulnerability_model.py | 7 +- .../config_based_model.py | 5 +- .../vulnerability_models/vulnerability.py | 45 +- tests/data/hazard_model_store_test.py | 14 +- .../config_based_uncertainty_test.py | 156 + .../config_based_vulnerability_test.py | 407 +- .../config_generate_test.py | 113 + 83 files changed, 3768 insertions(+), 1346 deletions(-) rename docs/{notebooks/2_asset_level_impacts.ipynb => examples/asset_level_impacts.ipynb} (100%) rename docs/{notebooks/1_hazard_indicators.ipynb => examples/hazard_indicators.ipynb} (100%) create mode 100644 docs/user-guide/introduction.ipynb rename docs/{notebooks/3_vulnerability_config.ipynb => user-guide/vulnerability_config.ipynb} (99%) rename {docs => methodology}/toadapt/README.md (100%) rename {docs => methodology}/toadapt/coastal_flood/damage_funs.png (100%) rename {docs => methodology}/toadapt/coastal_flood/exposure2.png (100%) rename {docs => methodology}/toadapt/commands.rst (100%) rename {docs => methodology}/toadapt/handbook/onboarding.rst (100%) rename {docs => methodology}/toadapt/handbook/onboarding/damage_with_uncertainty.pdf (100%) rename {docs => methodology}/toadapt/handbook/onboarding/damage_with_uncertainty.png (100%) rename {docs => methodology}/toadapt/handbook/onboarding/disruption_curve.pdf (100%) rename {docs => methodology}/toadapt/handbook/onboarding/disruption_curve.png (100%) rename {docs => methodology}/toadapt/handbook/onboarding/exceedance_curve.pdf (100%) rename {docs => methodology}/toadapt/handbook/onboarding/exceedance_curve.png (100%) rename {docs => methodology}/toadapt/handbook/onboarding/histo_from_exceedance.pdf (100%) rename {docs => methodology}/toadapt/handbook/onboarding/histo_from_exceedance.png (100%) rename {docs => methodology}/toadapt/handbook/onboarding/return_periods.pdf (100%) rename {docs => methodology}/toadapt/handbook/onboarding/return_periods.png (100%) rename {docs => methodology}/toadapt/handbook/onboarding/vulnerability_curve.pdf (100%) rename {docs => methodology}/toadapt/handbook/onboarding/vulnerability_curve.png (100%) rename {docs => methodology}/toadapt/handbook/onboarding/vulnerability_with_uncertainty.pdf (100%) rename {docs => methodology}/toadapt/handbook/onboarding/vulnerability_with_uncertainty.png (100%) rename {docs => methodology}/toadapt/hazards.rst (100%) rename {docs => methodology}/toadapt/hazards/README.md (100%) rename {docs => methodology}/toadapt/hazards/coastal_floods.rst (100%) rename {docs => methodology}/toadapt/hazards/landslide.rst (100%) rename {docs => methodology}/toadapt/hazards/references.bib (100%) rename {docs => methodology}/toadapt/hazards/riverine_floods.rst (100%) rename {docs => methodology}/toadapt/hazards/water_stress.rst (100%) rename {docs => methodology}/toadapt/hazards/wildfire.rst (100%) rename {docs => methodology}/toadapt/hazards/windstorm.rst (100%) rename {docs => methodology}/toadapt/landslide/LSImap.PNG (100%) rename {docs => methodology}/toadapt/landslide/land_cover.PNG (100%) rename {docs => methodology}/toadapt/landslide/landslide_classification.PNG (100%) rename {docs => methodology}/toadapt/landslide/landslide_matrix.PNG (100%) rename {docs => methodology}/toadapt/landslide/lithology.PNG (100%) rename {docs => methodology}/toadapt/landslide/terrain.PNG (100%) rename {docs => methodology}/toadapt/methodology/PRmodelling.png (100%) rename {docs => methodology}/toadapt/methodology/ex_curve.png (100%) rename {docs => methodology}/toadapt/methodology/flood_impact_JRC.png (100%) rename {docs => methodology}/toadapt/methodology/histogram.png (100%) rename {docs => methodology}/toadapt/methodology/intensity2.png (100%) rename {docs => methodology}/toadapt/river_flood/damage_function_spain_riverflood.png (100%) rename {docs => methodology}/toadapt/river_flood/damage_funs.png (100%) rename {docs => methodology}/toadapt/river_flood/exposure2.png (100%) rename {docs => methodology}/toadapt/river_flood/impact3.png (100%) rename {docs => methodology}/toadapt/river_flood/intensity2.png (100%) rename {docs => methodology}/toadapt/river_flood/intensity2_riverflood.png (100%) rename {docs => methodology}/toadapt/waterStress/waterstress1.PNG (100%) rename {docs => methodology}/toadapt/waterStress/waterwithdrawal.PNG (100%) rename {docs => methodology}/toadapt/wildfire/2. damage_function.png (100%) rename {docs => methodology}/toadapt/wildfire/EAI_map.png (100%) rename {docs => methodology}/toadapt/wildfire/damage_funs.png (100%) rename {docs => methodology}/toadapt/wildfire/exposure2.png (100%) rename {docs => methodology}/toadapt/wildfire/impact3.png (100%) rename {docs => methodology}/toadapt/wildfire/intensity2.png (100%) rename {docs => methodology}/toadapt/wildfire/powerplants.png (100%) rename {docs => methodology}/toadapt/wildfire/wildfire_damage.png (100%) rename {docs => methodology}/toadapt/wildfire/wildfire_impact.png (100%) rename {docs => methodology}/toadapt/wildfire/wildfire_intensity.png (100%) rename {docs => methodology}/toadapt/wildfire/wildfires_intensity2.png (100%) rename {docs => methodology}/toadapt/windstorm/damage_funs_wind3.png (100%) rename {docs => methodology}/toadapt/windstorm/exposure2.png (100%) rename {docs => methodology}/toadapt/windstorm/wind_damage.png (100%) rename {docs => methodology}/toadapt/windstorm/wind_impact_4TU.png (100%) rename {docs => methodology}/toadapt/windstorm/wind_intensity5.png (100%) create mode 100644 tests/vulnerability_models/config_based_uncertainty_test.py create mode 100644 tests/vulnerability_models/config_generate_test.py diff --git a/.gitignore b/.gitignore index 8a5814f8..f3899faf 100644 --- a/.gitignore +++ b/.gitignore @@ -45,6 +45,7 @@ htmlcov/ nosetests.xml coverage.xml *.cover +tests/working/* # Translations *.mo @@ -96,6 +97,7 @@ target/ src/test/data/coords.json credentials.env .pdm-python +.python-version # Latex methodology/PhysicalRiskMethodology.aux diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ef84e231..10d2d73c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -19,7 +19,7 @@ repos: rev: v1.5.5 hooks: - id: remove-tabs - exclude: ^.*\.(tsv)$ + exclude: ^.*(\.(tsv)|Makefile)$ - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.5.7 diff --git a/docs/Makefile b/docs/Makefile index 56664458..d4bb2cbb 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -10,11 +10,11 @@ BUILDDIR = _build # Put it first so that "make" without argument is like "make help". help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/notebooks/2_asset_level_impacts.ipynb b/docs/examples/asset_level_impacts.ipynb similarity index 100% rename from docs/notebooks/2_asset_level_impacts.ipynb rename to docs/examples/asset_level_impacts.ipynb diff --git a/docs/notebooks/1_hazard_indicators.ipynb b/docs/examples/hazard_indicators.ipynb similarity index 100% rename from docs/notebooks/1_hazard_indicators.ipynb rename to docs/examples/hazard_indicators.ipynb diff --git a/docs/getting-started.rst b/docs/getting-started.rst index 46bacff5..13d6306d 100644 --- a/docs/getting-started.rst +++ b/docs/getting-started.rst @@ -4,5 +4,5 @@ Getting started .. toctree:: :maxdepth: 1 - notebooks/1_hazard_indicators - notebooks/2_asset_level_impacts + examples/hazard_indicators + examples/asset_level_impacts diff --git a/docs/index.rst b/docs/index.rst index 7f449c1e..ebc910ca 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -95,7 +95,7 @@ Contents ========== .. toctree:: - :maxdepth: 2 + :maxdepth: 2 getting-started methodology diff --git a/docs/requirements-docs.txt b/docs/requirements-docs.txt index 475a0ce6..f0d5a567 100644 --- a/docs/requirements-docs.txt +++ b/docs/requirements-docs.txt @@ -1,27 +1,218 @@ -sphinx==5.3.0 -sphinxcontrib-details-directive +# This file is @generated by PDM. +# Please do not edit it manually. + +accessible-pygments==0.0.5 +affine==2.4.0 +aiobotocore==2.15.0 +aiohappyeyeballs==2.4.0 +aiohttp==3.10.5 +aioitertools==0.12.0 +aiosignal==1.3.1 +alabaster==0.7.16 +annotated-types==0.7.0 +anyio==4.4.0 +apeye==1.4.1 +apeye-core==1.1.5 +appdirs==1.4.4 +appnope==0.1.4; platform_system == "Darwin" +asciitree==0.3.3 +asttokens==2.4.1 +async-timeout==4.0.3; python_version < "3.11" +attrs==24.2.0 +autodocsumm==0.2.13 +babel==2.16.0 +beautifulsoup4==4.12.3 +bleach==6.1.0 +blinker==1.8.2 +botocore==1.35.16 +brotli==1.1.0; platform_python_implementation == "CPython" +brotlicffi==1.1.0.0; platform_python_implementation != "CPython" +cachecontrol[filecache]==0.14.0 +cachetools==5.5.0 +certifi==2024.8.30 +cffi==1.17.1 +cfgv==3.4.0 +chardet==5.2.0 +charset-normalizer==3.3.2 +click==8.1.7 +colorama==0.4.6 +comm==0.2.2 +coverage[toml]==7.6.1 +cssselect2==0.7.0 +cssutils==2.11.1 +debugpy==1.8.5 +decorator==5.1.1 +defusedxml==0.7.1 +dep-logic==0.4.6 +dependency-injector==4.42.0 +dict2css==0.3.0.post1 +distlib==0.3.8 +docutils==0.20.1 +domdf-python-tools==3.9.0 +exceptiongroup==1.2.2; python_version < "3.11" +executing==2.1.0 +fasteners==0.19; sys_platform != "emscripten" +fastjsonschema==2.20.0 +filelock==3.16.0 +findpython==0.6.1 +flexcache==0.3 +flexparser==0.3.1 +fonttools[woff]==4.53.1 +frozenlist==1.4.1 +fsspec==2024.9.0 +geopandas==1.0.1 +graphviz==0.20.3 +greenlet==3.1.0; (platform_machine == "win32" or platform_machine == "WIN32" or platform_machine == "AMD64" or platform_machine == "amd64" or platform_machine == "x86_64" or platform_machine == "ppc64le" or platform_machine == "aarch64") and python_version < "3.13" +h11==0.14.0 +h3==3.7.7 +hishel==0.0.30 +html5lib==1.1 +httpcore==1.0.5 +httpx[socks]==0.27.2 +identify==2.6.0 +idna==3.9 +imagesize==1.4.1 +importlib-metadata==8.5.0 +iniconfig==2.0.0 +installer==0.7.0 +ipykernel==6.29.5 +ipython==8.18.1 +jedi==0.19.1 +jinja2==3.1.4 +jmespath==1.0.1 +jsonschema==4.23.0 +jsonschema-specifications==2023.12.1 +jupyter-cache==1.0.0 +jupyter-client==8.6.2 +jupyter-core==5.7.2 +jupyterlab-pygments==0.3.0 +latexcodec==3.0.0 +libsass==0.23.0 +llvmlite==0.43.0 +lmdb==1.5.1 +lmdbm==0.0.6 +markdown-it-py==3.0.0 +markupsafe==2.1.5 +matplotlib-inline==0.1.7 +mdit-py-plugins==0.4.2 +mdurl==0.1.2 +mistune==3.0.2 +more-itertools==10.5.0 +msgpack==1.1.0 +multidict==6.1.0 +mypy==1.11.2 +mypy-extensions==1.0.0 +myst-nb==1.1.1 +myst-parser==3.0.1 +natsort==8.4.0 +nbclient==0.10.0 +nbconvert==7.16.4 +nbformat==5.10.4 +nbsphinx==0.9.5 +nest-asyncio==1.6.0 +nodeenv==1.9.1 +numba==0.60.0 +numcodecs==0.12.1 +numpy==2.0.2 numpydoc==1.8.0 -sphinx-copybutton==0.5.0 -myst-nb==0.16.0 -pydata-sphinx-theme==0.15.3 -sphinxcontrib-bibtex -sphinx-toolbox -sphinx_toggleprompt==0.2.0 -sphinx_design==0.3 -sphinx_rtd_theme -sphinx-toolbox -sphinx-simplepdf -pandoc -nbsphinx -graphviz -affine==2.3.0 -numpy==1.22.0 -pydantic==1.10.13 +packaging==24.1 +pandas==2.2.2 +pandoc==2.4 +pandocfilters==1.5.1 +parso==0.8.4 +pbs-installer==2024.9.9 +pdm[pytest]==2.18.2 +pexpect==4.9.0; sys_platform != "win32" +pillow==10.4.0 +pint==0.24.3 +platformdirs==4.3.3 +pluggy==1.5.0 +plumbum==1.8.3 +ply==3.11 +pre-commit==3.8.0 +prompt-toolkit==3.0.47 +psutil==6.0.0 +ptyprocess==0.7.0; sys_platform != "win32" +pure-eval==0.2.3 +pybtex==0.24.0 +pybtex-docutils==1.0.3 +pycparser==2.22 +pydantic==2.9.1 +pydantic-core==2.23.3 +pydata-sphinx-theme==0.15.4 +pydyf==0.11.0 +pygments==2.18.0 +pyogrio==0.9.0 +pyphen==0.16.0 +pyproj==3.6.1 +pyproject-api==1.7.1 +pyproject-hooks==1.1.0 +pytest==8.3.3 +pytest-cov==5.0.0 +pytest-mock==3.14.0 +python-dateutil==2.9.0.post0 python-dotenv==1.0.1 +pytz==2024.2 +pywin32==306; platform_system == "Windows" and platform_python_implementation != "PyPy" or sys_platform == "win32" and platform_python_implementation != "PyPy" +pyyaml==6.0.2 +pyzmq==26.2.0 +referencing==0.35.1 requests==2.32.3 -scipy==1.11.1 -s3fs==2022.1.0 +resolvelib==1.0.1 +rich==13.8.1 +rpds-py==0.20.0 +ruamel-yaml==0.18.6 +ruamel-yaml-clib==0.2.8; platform_python_implementation == "CPython" and python_version < "3.13" +ruff==0.6.5 +s3fs==2024.9.0 +scipy==1.13.1 +shapely==2.0.6 +shellingham==1.5.4 +six==1.16.0 +sniffio==1.3.1 +snowballstemmer==2.2.0 +socksio==1.0.0 +soupsieve==2.6 +sphinx==7.4.7 +sphinx-autodoc-typehints==2.3.0 +sphinx-copybutton==0.5.2 +sphinx-design==0.6.1 +sphinx-jinja2-compat==0.3.0 +sphinx-prompt==1.8.0 +sphinx-rtd-theme==2.0.0 +sphinx-simplepdf==1.6.0 +sphinx-tabs==3.4.5 +sphinx-toggleprompt==0.5.2 +sphinx-toolbox==3.8.0 +sphinxcontrib-applehelp==2.0.0 +sphinxcontrib-bibtex==2.6.3 +sphinxcontrib-details-directive==0.1.0 +sphinxcontrib-devhelp==2.0.0 +sphinxcontrib-htmlhelp==2.1.0 +sphinxcontrib-jquery==4.1 +sphinxcontrib-jsmath==1.0.1 +sphinxcontrib-qthelp==2.0.0 +sphinxcontrib-serializinghtml==2.0.0 +sqlalchemy==2.0.34 +stack-data==0.6.3 +tabulate==0.9.0 +tinycss2==1.3.0 +tomli==2.0.1; python_version < "3.11" +tomlkit==0.13.2 +tornado==6.4.1 +tox==4.18.1 +traitlets==5.14.3 +truststore==0.9.2; python_version >= "3.10" +typing-extensions==4.12.2 +tzdata==2024.1 +unearth==0.17.2 +urllib3==1.26.20 +virtualenv==20.26.4 +wcwidth==0.2.13 +weasyprint==62.3 +webencodings==0.5.1 +wrapt==1.16.0 +yarl==1.11.1 zarr==2.18.2 -pillow==10.3.0 -dependency-injector==4.41.0 -numba==0.60.0 +zipp==3.20.2 +zopfli==0.2.3 diff --git a/docs/user-guide.rst b/docs/user-guide.rst index 1e902c29..62820d72 100644 --- a/docs/user-guide.rst +++ b/docs/user-guide.rst @@ -1,64 +1,13 @@ User guide -=============== +========== If you are looking for the methodology of Physrisk or information about the different hazard indicators and vulnerability models, please check the `methodology document `_. If it is not library-related, it should be there. Please see also `GitHub Contributing `_ for how to get set up. -This section documents the structures and conventions of Physrisk and assumes some familiarity with the calculation of Physical Climate Risk (see `methodology document `_ introduction). +The following sections document the structures and conventions of Physrisk and assumes some familiarity with the calculation of Physical Climate Risk (see `methodology document `_ introduction). -Introduction to Physrisk ------------------------- -Physic comprises: -* A :code:`HazardModel` that retrieves *hazard indicators* for different locations. -* :code:`VulnerabilityModels` that assess the vulnerability of assets to different climate hazards. :code:`VulnerabilityModels` use hazard indicators requested from the :code:`HazardModel` to calculate the *impact* of a hazard on a collection of assets. -* Financial models that use the impacts calculated by the :code:`VulnerabilityModels` to calculate risk measures and scores. +.. toctree:: + :maxdepth: 1 -:code:`VulnerabilityModels` request hazard indicators using an :code:`indicator_id` (e.g. 'flood_depth' for inundation, 'max_speed' for wind). It is the responsibility of the :code:`HazardModel` to select the source of the hazard indicator data. Although :code:`VulnerabilityModels` can request specific hazard indicators by using a :code:`HazardDataHint` (e.g. a flood depth data set generated by a particular organization), this is generally discouraged: the idea is that a vulnerability model works with any data set. Note that units of the quantity are provided to the :code:`VulnerabilityModel` by the :code:`HazardModel`. - -Hazard indicator data sets -------------------------- -The :code:`HazardModel` retrieves hazard indicators in a number of ways and can be made composite in order to combine different ways of accessing the data. At time of writing the common cases are that: - -1. Hazard indicator data is stored in `Zarr `_ format (in an arbitrary Zarr store, although S3 is a popular choice). -2. Hazard indicator data is retrieved via call to an external API. This is mainly used when combining commercial data to the public-domain. - -In case 1, hazard indicators are stored as three dimensional arrays. The array is ordered :math:`(z, y, x)` where :math:`y` is the spatial :math:`y` coordinate, :math:`x` is the spatial :math:`x` coordinate and :math:`z` is an *index* coordinate. The *index* takes on different meanings according to the type of data being stored. - -Indicators can be either: - -* Acute (A): the data comprises a set of hazard intensities for different return periods. In this case *index* refers to the different return periods. -* Parametric (P): the data comprises a set of parameters. Here *index* refers to the different parameters. The parameters may be single values, or *index* might refer to a set of thresholds. Parametric indicators are used for chronic hazards. - -As mentioned above, a :code:`VulnerabilityModel` only specifies the identifier of the hazard indicator that is required, as well as the climate scenario ID and the year of the future projection (in the common case where hints are not used). This means that hazard indicator ID uniquely defines the data. For example, a vulnerability model requesting 'flood depth' could have data returned from a variety of data sets, depending on how the :code:`HazardModel` is configured. - -In case of stored-down (or 'pre-generated) hazard indicator data, as opposed to data source via an API, configuring which data sets to use is done by defining a :code:`SourcePath`. This is a function that provides the path, a unique identifier, that provides the data set to be used based on the hazard indicator ID, scenario and year. A convenience class :code:`CoreInventorySourcePaths` creates :code:`SourcePath` instances based on a set of rules called 'selectors'. A selector is a rule such as 'use the flood data set from this particular institution'. - -+-----------------------+-------------------------------+---------------------------------------+ -| Hazard class | Indicator ID (type) | Description | -+=======================+===============================+=======================================+ -| CoastalInundation, | flood_depth (A) | Flood depth (m) for available | -| PluvialInundation, | | return periods. This is unprotected | -| RiverineInundation | | depth. | -| +-------------------------------+---------------------------------------+ -| | sop (P) | Standard of protection | -| | | (as return period in years). | -+-----------------------+-------------------------------+---------------------------------------+ -| Fire | fire_probability (P) | Annual probability that location | -| | | is in a wildfire zone. | -+-----------------------+-------------------------------+---------------------------------------+ -| Heat | mean_degree_days/above/index | Mean mean-temperature degree days per | -| | (P) | year above a set of temperature | -| | | threshold indices. | -+-----------------------+-------------------------------+---------------------------------------+ -| Drought | months/spei/12m/below/index | Mean months per year where the 12 | -| | (P) | month SPEI index is below a set of | -| | | indices. | -+-----------------------+-------------------------------+---------------------------------------+ -| Wind | max_speed | Maximum 1 minute sustained wind speed | -| | (A) | for available return periods. | -+-----------------------+-------------------------------+---------------------------------------+ - - -Event based modelling ---------------------- -The most common use case for physrisk at time of writing is to perform analyses of portfolios but with asset impacts treated separately or with heuristics defining the dependence of asset impacts. In such cases it is possible, and indeed efficient, to derive the marginal probability distribution of the impact of the asset, the :code:`ImpactDistrib`. However to capture more realistic dependence of impacts, event-based modelling is needed. Here the :code:`HazardModel` additionally supplies an array of simulated hazard indicator values to the :code:`VulnerabilityModel` for each asset location, which in turn samples an array of impacts from the vulnerability function. At time of writing, the event-based functionality is not merged into main. Although specific types of :code:`HazardModel` and :code:`VulnerabilityModel` are needed for the event-based case, these do not replace the existing calculation which is optimized. + user-guide/introduction + user-guide/vulnerability_config diff --git a/docs/user-guide/introduction.ipynb b/docs/user-guide/introduction.ipynb new file mode 100644 index 00000000..4b894195 --- /dev/null +++ b/docs/user-guide/introduction.ipynb @@ -0,0 +1,64 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction to Physrisk\n", + "Physrisk comprises:\n", + "\n", + "* A `HazardModel` that retrieves *hazard indicators* for different locations.\n", + "* `VulnerabilityModels` that assess the vulnerability of assets to different climate hazards. `VulnerabilityModels` use hazard indicators requested from the `HazardModel` to calculate the *impact* of a hazard on a collection of assets.\n", + "* Financial models that use the impacts calculated by the `VulnerabilityModels` to calculate risk measures and scores.\n", + "\n", + "`VulnerabilityModels` request hazard indicators using an `indicator_id` (e.g. 'flood_depth' for inundation, 'max_speed' for wind). It is the responsibility of the `HazardModel` to select the source of the hazard indicator data. Although `VulnerabilityModels` can request specific hazard indicators by using a `HazardDataHint` (e.g. a flood depth data set generated by a particular organization), this is generally discouraged: the idea is that a vulnerability model works with any data set. Note that units of the quantity are provided to the `VulnerabilityModel` by the `HazardModel`.\n", + "\n", + "## Hazard indicator data sets\n", + "The `HazardModel` retrieves hazard indicators in a number of ways and can be made composite in order to combine different ways of accessing the data. At time of writing the common cases are that:\n", + "\n", + "1. Hazard indicator data is stored in [Zarr](https://zarr.readthedocs.io/en/stable/) format (in an arbitrary Zarr store, although S3 is a popular choice).\n", + "2. Hazard indicator data is retrieved via call to an external API. This is mainly used when combining commercial data to the public-domain.\n", + "\n", + "In case 1, hazard indicators are stored as three dimensional arrays. The array is ordered $(z, y, x)$ where $y$ is the spatial $y$ coordinate, $x$ is the spatial $x$ coordinate and $z$ is an *index* coordinate. The *index* takes on different meanings according to the type of data being stored.\n", + "\n", + "Indicators can be either:\n", + "\n", + "* Acute (A): the data comprises a set of hazard intensities for different return periods. In this case *index* refers to the different return periods.\n", + "* Parametric (P): the data comprises a set of parameters. Here *index* refers to the different parameters. The parameters may be single values, or *index* might refer to a set of thresholds. Parametric indicators are used for chronic hazards.\n", + "\n", + "As mentioned above, a `VulnerabilityModel` only specifies the identifier of the hazard indicator that is required, as well as the climate scenario ID and the year of the future projection (in the common case where hints are not used). This means that hazard indicator ID uniquely defines the data. For example, a vulnerability model requesting 'flood depth' could have data returned from a variety of data sets, depending on how the `HazardModel` is configured.\n", + "\n", + "In case of stored-down (or 'pre-generated) hazard indicator data, as opposed to data source via an API, configuring which data sets to use is done by defining a `SourcePath`. This is a function that provides the path, a unique identifier, that provides the data set to be used based on the hazard indicator ID, scenario and year. A convenience class `CoreInventorySourcePaths` creates `SourcePath` instances based on a set of rules called 'selectors'. A selector is a rule such as 'use the flood data set from this particular institution'. \n", + "\n", + "\n", + "| Hazard class | Indicator ID (type) | Description |\n", + "|---|---|---|\n", + "| CoastalInundation,
PluvialInundation,
RiverineInundation | flood_depth (A) | Unprotected flood depth (m) for available return periods. |\n", + "| CoastalInundation,
PluvialInundation,
RiverineInundation | sop (P) | Standard of protection as return period in years. |\n", + "| Fire | fire_probability (P) | Annual probability that location is in a wildfire zone. |\n", + "| Heat | mean_degree_days/above/index (p) | Mean mean-temperature degree days per year above a set of temperature threshold indices. |\n", + "| Drought | months/spei/12m/below/index (P) | Mean months per year where the 12 month SPEI index is below a set of indices. | \n", + "| Wind | max_speed (A) | Maximum 1 minute sustained wind speed for available return periods. |\n", + "\n", + "\n", + "Event based modelling\n", + "---------------------\n", + "The most common use case for physrisk at time of writing is to perform analyses of portfolios but with asset impacts treated separately or with heuristics defining the dependence of asset impacts. In such cases it is possible, and indeed efficient, to derive the marginal probability distribution of the impact of the asset, the `ImpactDistrib`. However to capture more realistic dependence of impacts, event-based modelling is needed. Here the `HazardModel` additionally supplies an array of simulated hazard indicator values to the `VulnerabilityModel` for each asset location, which in turn samples an array of impacts from the vulnerability function. At time of writing, the event-based functionality is not merged into main. Although specific types of `HazardModel` and `VulnerabilityModel` are needed for the event-based case, these do not replace the existing calculation which is optimized. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/3_vulnerability_config.ipynb b/docs/user-guide/vulnerability_config.ipynb similarity index 99% rename from docs/notebooks/3_vulnerability_config.ipynb rename to docs/user-guide/vulnerability_config.ipynb index 760aff5c..4cc3bafd 100644 --- a/docs/notebooks/3_vulnerability_config.ipynb +++ b/docs/user-guide/vulnerability_config.ipynb @@ -6,7 +6,6 @@ "metadata": {}, "outputs": [], "source": [ - "# UNCOMMENT TO RUN THIS NOTEBOOK\n", "# pip install nbformat pandas plotly requests\n", "from io import StringIO\n", "\n", diff --git a/docs/toadapt/README.md 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methodology/toadapt/handbook/onboarding.rst diff --git a/docs/toadapt/handbook/onboarding/damage_with_uncertainty.pdf b/methodology/toadapt/handbook/onboarding/damage_with_uncertainty.pdf similarity index 100% rename from docs/toadapt/handbook/onboarding/damage_with_uncertainty.pdf rename to methodology/toadapt/handbook/onboarding/damage_with_uncertainty.pdf diff --git a/docs/toadapt/handbook/onboarding/damage_with_uncertainty.png b/methodology/toadapt/handbook/onboarding/damage_with_uncertainty.png similarity index 100% rename from docs/toadapt/handbook/onboarding/damage_with_uncertainty.png rename to methodology/toadapt/handbook/onboarding/damage_with_uncertainty.png diff --git a/docs/toadapt/handbook/onboarding/disruption_curve.pdf b/methodology/toadapt/handbook/onboarding/disruption_curve.pdf similarity index 100% rename from docs/toadapt/handbook/onboarding/disruption_curve.pdf rename to methodology/toadapt/handbook/onboarding/disruption_curve.pdf diff --git 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similarity index 100% rename from docs/toadapt/windstorm/exposure2.png rename to methodology/toadapt/windstorm/exposure2.png diff --git a/docs/toadapt/windstorm/wind_damage.png b/methodology/toadapt/windstorm/wind_damage.png similarity index 100% rename from docs/toadapt/windstorm/wind_damage.png rename to methodology/toadapt/windstorm/wind_damage.png diff --git a/docs/toadapt/windstorm/wind_impact_4TU.png b/methodology/toadapt/windstorm/wind_impact_4TU.png similarity index 100% rename from docs/toadapt/windstorm/wind_impact_4TU.png rename to methodology/toadapt/windstorm/wind_impact_4TU.png diff --git a/docs/toadapt/windstorm/wind_intensity5.png b/methodology/toadapt/windstorm/wind_intensity5.png similarity index 100% rename from docs/toadapt/windstorm/wind_intensity5.png rename to methodology/toadapt/windstorm/wind_intensity5.png diff --git a/pdm.lock b/pdm.lock index a63b0ab0..fce8064f 100644 --- a/pdm.lock +++ b/pdm.lock @@ -2,10 +2,27 @@ # It is not intended for manual editing. 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] [tool.pdm.scripts] @@ -106,7 +123,9 @@ pre_release = "scripts/dev-versioning.sh" release = "scripts/release-versioning.sh" test = "pytest" tox = "tox" +docs_requirements = { shell = "pdm export -o docs/requirements-docs.txt --no-hashes", help = "Update requirements-docs.txt" } docs = { shell = "cd docs && make html", help = "Start the dev server for doc preview" } +open_docs = { shell = "open ./docs/_build/html/index.html", help = "Start the dev server for doc preview" } lint = "pre-commit run --all-files" complete = { call = "tasks.complete:main", help = "Create autocomplete files for bash and fish" } all = {composite = ["pytest", "lint"]} diff --git a/src/physrisk/kernel/vulnerability_model.py b/src/physrisk/kernel/vulnerability_model.py index 6886d076..6b47964c 100644 --- a/src/physrisk/kernel/vulnerability_model.py +++ b/src/physrisk/kernel/vulnerability_model.py @@ -67,7 +67,12 @@ def delta_cdf(y): def checked_beta_distrib(mean, std, scaling_factor=1.0): if std == 0 or mean == 0 or mean == scaling_factor: return delta_cdf(mean) - return beta_distrib(mean, std, scaling_factor) + cv = std / mean + a = ((scaling_factor - mean) / (cv * cv) - mean) / scaling_factor + b = a * (scaling_factor - mean) / mean + if a <= 0 or b <= 0: + return delta_cdf(mean) + return lambda x, a=a, b=b: stats.beta.cdf(x / scaling_factor, a, b) def beta_distrib(mean, std, scaling_factor): diff --git a/src/physrisk/vulnerability_models/config_based_model.py b/src/physrisk/vulnerability_models/config_based_model.py index 5306e3ed..24f3a7ee 100644 --- a/src/physrisk/vulnerability_models/config_based_model.py +++ b/src/physrisk/vulnerability_models/config_based_model.py @@ -494,7 +494,6 @@ def __init__( hazard_class: type, # type of the hazard class impact_id: str, config_items: Sequence[VulnerabilityConfigItem], - disable_api_calls: bool, ): """A vulnerability model that requires only specification of a set of damage/disruption curves which are provided by configuration. This simple model contains no uncertainty around damage/disruption. @@ -514,8 +513,8 @@ def __init__( ImpactCurveKey(item.asset_identifier): ImpactCurve.get_impact_curve(item) for item in config_items } - self.has_sop = not (disable_api_calls) and ( - (hazard_class == RiverineInundation) or (hazard_class == PluvialInundation) + self.has_sop = (hazard_class == RiverineInundation) or ( + hazard_class == PluvialInundation ) self.asset_attributes = set( key for curve in self.curves for key in curve.identifier.keys() diff --git a/src/physrisk/vulnerability_models/vulnerability.py b/src/physrisk/vulnerability_models/vulnerability.py index c644ce20..9a049942 100644 --- a/src/physrisk/vulnerability_models/vulnerability.py +++ b/src/physrisk/vulnerability_models/vulnerability.py @@ -5,7 +5,7 @@ from operator import add from typing import Dict, List, Optional, Sequence, Set, Type -from physrisk.kernel.assets import Asset, RealEstateAsset, ThermalPowerGeneratingAsset +from physrisk.kernel.assets import Asset from physrisk.kernel.hazards import HazardKind from physrisk.kernel.vulnerability_model import VulnerabilityModelBase from physrisk.kernel.vulnerability_model import ( @@ -14,21 +14,6 @@ from physrisk.kernel.vulnerability_model import ( VulnerabilityModelsFactory as PVulnerabilityModelsFactory, ) -from physrisk.vulnerability_models.real_estate_models import ( # CoolingModel, - GenericTropicalCycloneModel, - RealEstateCoastalInundationModel, - RealEstatePluvialInundationModel, - RealEstateRiverineInundationModel, -) -from physrisk.vulnerability_models.thermal_power_generation_models import ( - ThermalPowerGenerationAirTemperatureModel, - ThermalPowerGenerationCoastalInundationModel, - ThermalPowerGenerationDroughtModel, - ThermalPowerGenerationRiverineInundationModel, - ThermalPowerGenerationWaterStressModel, - ThermalPowerGenerationWaterTemperatureModel, -) - import physrisk.data from physrisk.vulnerability_models.config_based_model import ( ConfigBasedVulnerabilityModel, @@ -38,7 +23,6 @@ ) physrisk_assets = import_module("physrisk.kernel.assets") -# physrisk_assets = import_module("physrisk.assets") def get_asset_type(asset_class: str): @@ -79,7 +63,7 @@ def __init__( self.models: Dict[Type[Asset], Sequence[VulnerabilityModelBase]] = ( self.programmatic_models() ) - # the programmatic models always 'win' + # the config-based models always 'win' # add any config based models to the programmatic models if any(config): config_based_models = self.config_based_models(config) @@ -147,10 +131,10 @@ def __init__( raise ValueError("") def vuln_model_for_asset_of_type( - self, type: type + self, type: Type[Asset] ) -> Sequence[VulnerabilityModelBase]: # models = self.models.get(Asset) - models = self.models[Asset] + models = self.models.get(Asset) for asset_class_type in type.mro(): if asset_class_type is not object and asset_class_type in self.models: models = self.models[asset_class_type] # type:ignore @@ -161,8 +145,6 @@ def vuln_model_for_asset_of_type( return models def config_based_models(self, config: Sequence[VulnerabilityConfigItem]): - # physrisk_assets = import_module("physrisk.kernel.assets") - # physrisk_assets = import_module("physrisk.kernel.assets") physrisk_hazards = import_module("physrisk.kernel.hazards") # Each asset class has its own set of damage curves. The class has coarse granularity, # e.g. RealEstateAsset, PowerGeneratingAsset @@ -240,7 +222,6 @@ def config_based_models(self, config: Sequence[VulnerabilityConfigItem]): hazard_class=hazard_class, impact_id=key.impact_id, config_items=grouped_items[key], - disable_api_calls=self.disable_api_calls, ) ) else: @@ -262,20 +243,4 @@ def config_based_models(self, config: Sequence[VulnerabilityConfigItem]): def programmatic_models( self, ) -> Dict[Type[Asset], Sequence[VulnerabilityModelBase]]: - return { - RealEstateAsset: [ - RealEstateCoastalInundationModel(), - RealEstatePluvialInundationModel(), - RealEstateRiverineInundationModel(), - GenericTropicalCycloneModel(), - # CoolingModel(), - ], - ThermalPowerGeneratingAsset: [ - ThermalPowerGenerationAirTemperatureModel(), - ThermalPowerGenerationCoastalInundationModel(), - ThermalPowerGenerationDroughtModel(), - ThermalPowerGenerationRiverineInundationModel(), - ThermalPowerGenerationWaterStressModel(), - ThermalPowerGenerationWaterTemperatureModel(), - ], - } + return {} diff --git a/tests/data/hazard_model_store_test.py b/tests/data/hazard_model_store_test.py index ab6933be..92f14fe1 100644 --- a/tests/data/hazard_model_store_test.py +++ b/tests/data/hazard_model_store_test.py @@ -1,5 +1,5 @@ import os -from typing import Dict, List, Tuple, Union +from typing import Dict, List, Sequence, Tuple, Union import numpy as np import numpy.typing as npt @@ -40,8 +40,8 @@ def __init__(self): def add_curves_global( self, array_path: str, - longitudes: List[float], - latitudes: List[float], + longitudes: Sequence[float], + latitudes: Sequence[float], return_periods: Union[List[float], npt.NDArray], intensities: Union[List[float], npt.NDArray], width: int = 43200, @@ -75,13 +75,13 @@ def _crs_shape_transform_global( def _add_curves( self, array_path: str, - longitudes: List[float], - latitudes: List[float], + longitudes: Sequence[float], + latitudes: Sequence[float], crs: str, shape: Tuple[int, int, int], trans: List[float], - return_periods: Union[List[float], npt.NDArray], - intensities: Union[List[float], npt.NDArray], + return_periods: Union[Sequence[float], npt.NDArray], + intensities: Union[Sequence[float], npt.NDArray], units: str = "default", ): z = self._root.create_dataset( # type: ignore diff --git a/tests/vulnerability_models/config_based_uncertainty_test.py b/tests/vulnerability_models/config_based_uncertainty_test.py new file mode 100644 index 00000000..77042446 --- /dev/null +++ b/tests/vulnerability_models/config_based_uncertainty_test.py @@ -0,0 +1,156 @@ +from typing import Optional +from numba import float64, njit +from numba.experimental import jitclass +import numpy as np +import pytest + +from physrisk.kernel.vulnerability_model import checked_beta_distrib + + +@pytest.mark.skip("in dev") +def test_vuln_uncertainty(): + # hazard_indicator = [0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 6.0] + impact_mean = [0.0, 0.327, 0.494, 0.617, 0.721, 0.870, 0.931, 0.984, 1.0] + impact_stddev = [0.0, 0.25, 0.22, 0.21, 0.21, 0.17, 0.12, 0.048, 0.0] + cdf = CDFBasedVulnerabilityFunction.from_stddev( + impact_mean, impact_stddev, CDFBasedVulnerabilityFunction._default_impact_grid + ) + assert cdf is not None + + +class CDFBasedVulnerabilityFunction: + """In general the vulnerability curve specifies for each hazard intensity level a + cumulative probability density function. + More precisely: + The hazard intensity values, :math:`x_i`, are given, :math:`i \in [1 \dots n]`. + .. math:: F_i(y) = \mathbb{P}(Y \leq y|x_i) + + The CDF, :math:`F_i(y)`, is given for points :math:`y_j`, :math:`j \in [1 \dots m]`. + .. math:: + F_{ij} = \mathbb{P}(Y \leq y_j|x_i) + x = [x_1, x_2, \dots, x_n ] + y = [y_1, y_2, \dots, y_m ] + z = [[F_{11}, F_{12}, \dots, F_{1m}], [F_{21}, F_{12}, \dots, F_{2m}], \dots, [F_{n1}, F_{n2}, \dots, F_{nm}]] + + + Two cases: + 1) Hazard intensity, :math:`x'` is known (i.e. a drawing from an event set) + Here the CDF is interpolated from the CDFs bounding the :math:`x'`: + + .. math:: F_{x'}(y_j) = \frac{x_{k+1} - x'}{x_{k+1} - x_k} F_{kj} + \frac{x' - x_k}{x_{k+1} - x_k} F_{(k+1)j} + + 2) Probability is known within a range :math:(a, b). + Here the CDF within the interval is approximated by the CDF interpolated at the midpoint math:`x' = \frac{a + b}{2}`. + """ + + _default_impact_grid = np.array( + [0, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0] + ) + + __slots__ = "cdf", "impact", "haz_indicator", "kind" + + def __init__( + self, + x: np.ndarray, + y: np.ndarray, + z: Optional[np.ndarray], + kind: str = "deterministic", + impact_grid: Optional[np.ndarray] = _default_impact_grid, + ): + """Initialise a CDF + + Args: + x (np.ndarray): array_like, (`n`,). + Hazard indicator values. + y (np.ndarray): array_like, (`m`,). + Impact values if kind is 'deterministic', (`n`, ). + Mean impact values if kind is 'beta' or 'trunc_gauss', (`n`, ). + Impact values for CDF specified in `z` if kind is 'cdf', (`m`, ) + z (Optional[np.ndarray]): array_like. + Standard deviation of impact values if `kind` is 'beta' or 'trunc_gauss', + (`n`, ). + If `kind` is `cdf` then `z[i, j]` is the probability that if + the hazard indicator value is given by `x[i]` the impact is + `<= y[j]`, (`n`, `m`). + kind (str): 'deterministic', 'beta', 'trunc_gaussian', 'cdf'. Defaults to 'deterministic'. + impact_grid (np.ndarray): array_like, (`m`,). + If kind is 'beta' or 'trunc_gauss', defines the impact values on which + the CDF is calculated, otherwise unused. + the CDF is calculated, otherwise unused. + """ + if x.ndim != 1 or y.ndim != 1: + raise ValueError("`x` and `y` must be 1 dimensional.") + if kind in ["deterministic", "beta", "trunc_gauss"]: + if len(x) != len(y): + raise ValueError(f"`x` and `y` must have same length in `{kind}` case.") + if z is not None: + raise ValueError(f"`z` value is not used in `{kind}` case.") + if kind == "determinisitc": + self.haz_indicator = x.copy() + self.impact = y.copy() + else: + # TODO set intensity, impact and cdf + raise NotImplementedError() + + if kind == "cdf": + assert z is not None + if z.ndim != 2 or z.shape[0] != len(x) or z.shape[1] != len(y): + raise ValueError("`z` should be two dimensional with shape (`n`, `m`).") + self.haz_indicator = x.copy() + self.impact = y.copy() + self.cdf = z + + self.kind = kind + self.impact = impact_grid + + def interpolate_cdfs(self, haz_ind_values: np.ndarray) -> np.ndarray: + """Returns cumulative probabilities for the impact values for the hazard indicator values provided. + This is done by linearly interpolating between the cumulative probabilities of the 'pillar' + hazard indicators in self.haz_indicator. + + Args: + haz_ind_values (np.ndarray): Hazard indicator values. + + Returns: + np.ndarray: Array with size (len(haz_ind_values), len(impact)), i.e. a matrix with the CDFs + in the rows. + """ + left = np.searchsorted(self.haz_indicator, haz_ind_values, side="left") + right = left + 1 + cdfs = ( + (self.haz_indicator[right] - haz_ind_values) * self.cdf[left, :] + + (haz_ind_values - self.haz_indicator[left]) * self.cdf[right, :] + ) / (self.haz_indicator[right] - self.haz_indicator[left]) + cdfs[left == len(self.haz_indicator), :] = cdfs[len(self.haz_indicator) - 1, :] + return cdfs + + @staticmethod + def from_stddev( + impact_mean: np.ndarray, + impact_stddev: np.ndarray, + impact_grid: np.ndarray, + kind: str, + ): + if kind == "beta": + # this is done just once for curve + impact_cdfs = [ + checked_beta_distrib(m, s)(impact_grid) + for m, s in zip(impact_mean, impact_stddev) + ] + return impact_cdfs + else: + # TODO add support for "truncated_gaussian" + raise NotImplementedError() + + +@njit(cache=True) +def sample_from_cumulative_probs( + values: np.ndarray, cum_probs: np.ndarray, uniforms: np.ndarray +): + n = cum_probs.shape[0] + nb_samples = uniforms.shape[1] + assert uniforms.shape[0] == n + samples = np.zeros(shape=(n, nb_samples)) + for i in range(n): + samples[i, :] = np.interp(uniforms[i, :], cum_probs[i, :], values) + return samples diff --git a/tests/vulnerability_models/config_based_vulnerability_test.py b/tests/vulnerability_models/config_based_vulnerability_test.py index c665234f..b09bd18f 100644 --- a/tests/vulnerability_models/config_based_vulnerability_test.py +++ b/tests/vulnerability_models/config_based_vulnerability_test.py @@ -1,5 +1,5 @@ import pathlib -from typing import cast +from typing import Sequence, cast import numpy as np import pandas as pd @@ -17,6 +17,15 @@ RealEstateAsset, ThermalPowerGeneratingAsset, ) +from physrisk.kernel.hazards import ( + ChronicHeat, + CoastalInundation, + Drought, + Fire, + Hail, + RiverineInundation, + Wind, +) from physrisk.vulnerability_models.config_based_model import ( ConfigBasedVulnerabilityModelAcute, ImpactCurveKey, @@ -29,6 +38,7 @@ VulnerabilityModelsFactory, ) from tests.data.hazard_model_store_test import ( + ZarrStoreMocker, add_curves, shape_transform_21600_43200, zarr_memory_store, @@ -1090,6 +1100,96 @@ def config_based_vulnerability_models(): ] +def create_store(latitudes: Sequence[float], longitudes: Sequence[float]): + # fmt: off + flood_return_periods = [2.0, 5.0, 10.0, 25.0, 50.0, 100.0, 250.0, 500.0, 1000.0] + wind_return_periods=[20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000] + + # this is the best way to mock data: + source_paths = get_default_source_paths() + def hazard_path(hazard_type, indicator_id, scenario, year): + return source_paths[hazard_type]( + indicator_id=indicator_id, scenario=scenario, year=year + ) + + mocker = ZarrStoreMocker() + + mocker.add_curves_global( + hazard_path(RiverineInundation, "flood_depth", "historical", -1), + longitudes, + latitudes, + flood_return_periods, + np.array([0.0012, 0.39, 0.85, 1.39, 1.75, 2.09, 2.51, 2.82, 3.12]), + ) + mocker.add_curves_global( + hazard_path(RiverineInundation, "flood_depth", "ssp585", 2050), + longitudes, + latitudes, + flood_return_periods, + np.array([0.0012, 0.39, 0.85, 1.39, 1.75, 2.09, 2.51, 2.82, 3.12]), + ) + mocker.add_curves_global( + hazard_path(RiverineInundation, "flood_sop", "historical", -1), + longitudes, + latitudes, + [0.0, 0.0], + np.array([0.0, 0.0]), + ) + mocker.add_curves_global( + hazard_path(CoastalInundation, "flood_depth", "ssp585", 2050), + longitudes, + latitudes, + flood_return_periods, + np.array([0., 0., 0., 0., 0., 0., 0., 0., 0.]), + ) + mocker.add_curves_global( + hazard_path(Wind, "max_speed", "ssp585", 2050), + longitudes, + latitudes, + wind_return_periods, + np.array([24.44, 26.79, 28.63, 30.10, 31.03, 32.00, 33.04, 33.75, 34.23, 39.33, 43.0, 44.96, 46.19, 47.62, 48.58, 48.83, 49.79, 50.11]), + ) + mocker.add_curves_global( + hazard_path(ChronicHeat, "days_wbgt_above", "ssp585", 2050), + longitudes, + latitudes, + [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60], + np.array([363.65, 350.21, 303.64, 240.48, 181.83, 128.47, 74.40, 1.40, 0.0, 0.0, 0.0, 0.0]), + ) + mocker.add_curves_global( + hazard_path(Fire, "fire_probability", "ssp585", 2050), + longitudes, + latitudes, + [0], + np.array([0.31867]), + ) + mocker.add_curves_global( + hazard_path(Drought, "months/spei3m/below/-2", "ssp585", 2050), + longitudes, + latitudes, + [0], + np.array([0.17918]), + ) + mocker.add_curves_global( + hazard_path(Hail, "days/above/5cm", "ssp585", 2050), + longitudes, + latitudes, + [0], + np.array([0.67936]), + ) + for temp, value in zip([25, 30, 35, 40, 45, 50, 55], + [148.6, 65.31, 0.6, 0.0, 0.0, 0.0, 0.0]): + mocker.add_curves_global( + hazard_path(ChronicHeat, f"days_tas/above/{temp}c", "ssp585", 2050), + longitudes, + latitudes, + [0], + np.array([value]), + ) + # fmt:on + return mocker.store + + def test_create_vulnerability_model(): factory = VulnerabilityModelsFactory(config=basic_vulnerability_config()) vulnerability_models = factory.vulnerability_models() @@ -1189,238 +1289,7 @@ def test_config_based_vulnerability_models(working_dir): factory = VulnerabilityModelsFactory(config=config_items) vulnerability_models = factory.vulnerability_models(disable_api_calls=True) - store, root = zarr_memory_store() - - return_periods = [2.0, 5.0, 10.0, 25.0, 50.0, 100.0, 250.0, 500.0, 1000.0] - shape, t = shape_transform_21600_43200(return_periods=return_periods) - add_curves( - root, - longitudes, - latitudes, - "inundation/wri/v2/inunriver_rcp8p5_MIROC-ESM-CHEM_2050", - shape, - np.array( - [ - 0.0012, - 0.39, - 0.85, - 1.39, - 1.75, - 2.09, - 2.51, - 2.82, - 3.12, - ] - ), - return_periods, - t, - ) - add_curves( - root, - longitudes, - latitudes, - "inundation/wri/v2/inuncoast_rcp8p5_wtsub_2050_0", - shape, - np.array( - [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - ] - ), - return_periods, - t, - ) - - return_periods = [ - 20, - 30, - 40, - 50, - 60, - 70, - 80, - 90, - 100, - 200, - 300, - 400, - 500, - 600, - 700, - 800, - 900, - 1000, - ] - shape, t = shape_transform_21600_43200(return_periods=return_periods) - add_curves( - root, - longitudes, - latitudes, - "wind/iris/v1/max_speed_ssp585_2050", - shape, - np.array( - [ - 24.44, - 26.79, - 28.63, - 30.10, - 31.03, - 32.00, - 33.04, - 33.75, - 34.23, - 39.33, - 43.0, - 44.96, - 46.19, - 47.62, - 48.58, - 48.83, - 49.79, - 50.11, - ] - ), - return_periods, - t, - ) - - return_periods = [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60] - shape, t = shape_transform_21600_43200(return_periods=return_periods) - add_curves( - root, - longitudes, - latitudes, - "chronic_heat/osc/v2/days_wbgt_above_ACCESS-CM2_ssp585_2050", - shape, - np.array( - [ - 363.65, - 350.21, - 303.64, - 240.48, - 181.83, - 128.47, - 74.40, - 1.40, - 0.0, - 0.0, - 0.0, - 0.0, - ] - ), - return_periods, - t, - ) - - return_periods = [0.0] - shape, t = shape_transform_21600_43200(return_periods=return_periods) - add_curves( - root, - longitudes, - latitudes, - "fire/jupiter/v1/fire_probability_ssp585_2050", - shape, - np.array([0.31867]), - return_periods, - t, - ) - add_curves( - root, - longitudes, - latitudes, - "drought/jupiter/v1/months_spei3m_below_-2_ssp585_2050", - shape, - np.array([0.17918]), - return_periods, - t, - ) - add_curves( - root, - longitudes, - latitudes, - "hail/jupiter/v1/days_above_5cm_ssp585_2050", - shape, - np.array([0.67936]), - return_periods, - t, - ) - add_curves( - root, - longitudes, - latitudes, - "chronic_heat/osc/v2/days_tas_above_25c_ACCESS-CM2_ssp585_2050", - shape, - np.array([148.6]), - return_periods, - t, - ) - add_curves( - root, - longitudes, - latitudes, - "chronic_heat/osc/v2/days_tas_above_30c_ACCESS-CM2_ssp585_2050", - shape, - np.array([65.31]), - return_periods, - t, - ) - add_curves( - root, - longitudes, - latitudes, - "chronic_heat/osc/v2/days_tas_above_35c_ACCESS-CM2_ssp585_2050", - shape, - np.array([0.6]), - return_periods, - t, - ) - add_curves( - root, - longitudes, - latitudes, - "chronic_heat/osc/v2/days_tas_above_40c_ACCESS-CM2_ssp585_2050", - shape, - np.array([0.0]), - return_periods, - t, - ) - add_curves( - root, - longitudes, - latitudes, - "chronic_heat/osc/v2/days_tas_above_45c_ACCESS-CM2_ssp585_2050", - shape, - np.array([0.0]), - return_periods, - t, - ) - add_curves( - root, - longitudes, - latitudes, - "chronic_heat/osc/v2/days_tas_above_50c_ACCESS-CM2_ssp585_2050", - shape, - np.array([0.0]), - return_periods, - t, - ) - add_curves( - root, - longitudes, - latitudes, - "chronic_heat/osc/v2/days_tas_above_55c_ACCESS-CM2_ssp585_2050", - shape, - np.array([0.0]), - return_periods, - t, - ) + store = create_store(latitudes, longitudes) source_paths = get_default_source_paths(EmbeddedInventory()) @@ -1497,6 +1366,78 @@ def test_config_based_vulnerability_models(working_dir): np.testing.assert_almost_equal(impacts["RiverineInundation"], 0.043734252) +def test_config_based_acute_model_with_sop(): + """Test of config-based model including standard of protection information.""" + # inunriver_rcp4p5_MIROC-ESM-CHEM_2030 flood_sop_historical_1985 + + config_items = [ + VulnerabilityConfigItem( + hazard_class="RiverineInundation", # CoastalInundation,PluvialInundation,RiverineInundation + asset_class="RealEstateAsset", + asset_identifier="type=Buildings/Residential,location=North America", + indicator_id="flood_depth", + indicator_units="metres", + impact_id="damage", + impact_units=None, + curve_type="indicator/piecewise_linear", + points_x=[0.05, 0.5, 1, 1.5, 2, 3, 4, 5, 6], + points_y=[ + 0.20, + 0.44, + 0.58, + 0.68, + 0.78, + 0.85, + 0.924, + 0.959, + 1, + ], + activation_of_points_x=None, + cap_of_points_x=None, + cap_of_points_y=None, + ) + ] + + latitudes = np.array([32.6017]) + longitudes = np.array([-87.7811]) + assets = [ + RealEstateAsset( + location="North America", + latitude=latitudes[0], + longitude=longitudes[0], + type="Buildings/Residential", + ) + ] + factory = VulnerabilityModelsFactory(config=config_items) + vulnerability_models = factory.vulnerability_models(disable_api_calls=True) + + store = create_store(latitudes, longitudes) + + source_paths = get_default_source_paths(EmbeddedInventory()) + + request = requests.AssetImpactRequest( + assets=Assets(items=[]), + include_asset_level=True, + include_calc_details=True, + years=[2050], + scenarios=["ssp585"], + ) + + response = requests._get_asset_impacts( + request, + ZarrHazardModel(source_paths=source_paths, reader=ZarrReader(store)), + vulnerability_models=vulnerability_models, + assets=assets, + ) + + impacts = { + impact.hazard_type: impact.impact_mean + for impact in response.asset_impacts[0].impacts + } + # TODO complete test + assert impacts is not None + + @pytest.mark.skip("Only to be run when importing HAZUS damage functions.") def test_import_hazus_damage_function(working_dir): tab = "FL_BuildingDamageFns" diff --git a/tests/vulnerability_models/config_generate_test.py b/tests/vulnerability_models/config_generate_test.py new file mode 100644 index 00000000..cc2873d4 --- /dev/null +++ b/tests/vulnerability_models/config_generate_test.py @@ -0,0 +1,113 @@ +import json +import numpy as np +import pandas as pd + +from physrisk.kernel.assets import RealEstateAsset +from physrisk.kernel.hazards import ( + CoastalInundation, + PluvialInundation, + RiverineInundation, +) +from physrisk.kernel.impact_distrib import ImpactType +from physrisk.vulnerability_models.config_based_model import ( + VulnerabilityConfigItem, + config_items_to_csv, +) + + +def test_generate_all_config(vulnerability_onboarding_dir): + """Generate all vulnerability config. This is done via test because: + - this ensures that the config remains traceable to source and + - in general the process is not compute-intensive. + + Args: + vulnerability_onboarding_dir (Path): Path to vulnerability on-boarding data directory. + """ + config_items = generate_all_config(vulnerability_onboarding_dir) + config_items_to_csv( + config_items, vulnerability_onboarding_dir / "candidate_config.csv" + ) + + +def generate_all_config(vulnerability_onboarding_dir): + config_items: list[VulnerabilityConfigItem] = [] + config_items += flood_config_jrc(vulnerability_onboarding_dir) + return config_items + + +def flood_config_jrc(vulnerability_onboarding_dir): + """Create vulnerability config for flood based on the EU JRC global depth-damage functions. + This replaces the notebook implementation and sets the pattern for code-based generation of the config. + + Args: + vulnerability_onboarding_dir (_type_): Path to vulnerability on-boarding data directory. + """ + # raw data is actually a reformatting of the original Excel spreadsheet to facilitate loading. + raw_data = ( + vulnerability_onboarding_dir + / "EU JRC global flood depth-damage functions" + / "raw.csv" + ) + df = pd.read_csv(raw_data) + + # consistent with physrisk continent definition + location_mappings = { + "Europe": "Europe", + "North America": "North America", + "Central & South America": "South America", + "Asia": "Asia", + "Africa": "Africa", + "Oceania": "Oceania", + "Global": "Global", + } + type_mappings = { + "Residential buildings": "Buildings/Residential", + "Commercial buildings": "Buildings/Commercial", + "Industrial buildings": "Buildings/Industrial", + } + + config_items: list[VulnerabilityConfigItem] = [] + for mapping in type_mappings: + type_df = df[df["Type"] == mapping] + flood_depth = type_df["Flood depth [m]"].to_numpy() + for location in location_mappings: + # whether zero depth is considered really zero or a flood event with smallest depth + zero_as_minimum = True if location == "North America" else False + # for North America, the 0 depth damage is for flooding of any depth. We consider that a 1 cm inundation. + depth = ( + np.concatenate([[0, 0.01], flood_depth[1:]]) + if zero_as_minimum + else flood_depth + ) + + mean = type_df[location + "_Mean"].to_numpy() + std = type_df[location + "_Std"].to_numpy() + mean = np.concatenate([[0], mean]) if zero_as_minimum else mean + std = np.concatenate([[0], std]) if zero_as_minimum else std + if np.any(np.isnan(mean)): + mean = [] + if np.any(np.isnan(std)): + std = [] + if not any(mean): + continue + config_items.append( + VulnerabilityConfigItem( + hazard_class=",".join( + [ + CoastalInundation.__name__, + PluvialInundation.__name__, + RiverineInundation.__name__, + ] + ), + asset_class=RealEstateAsset.__name__, + asset_identifier=f"type={type_mappings[mapping]},location={location_mappings[location]}", + indicator_id="flood_depth", + indicator_units="metres", + impact_id=str(ImpactType.damage.name), + curve_type="indicator/piecewise_linear", + points_x=list(depth), + points_y=list(mean), + # points_z=std, # omit for now: until implemented + ) + ) + return config_items