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"source": [
"## Custom vulnerability functions for asset-level impact calculations\n",
"In this walk-through, custom vulnerability functions are defined and used to perform the asset-level impact calculations. The portfolio is identical to the one previously calculated; the difference is only in the config-based vulnerability.\n",
"\n",
"There is no option exposed to inject a custom vulnerability config via the API at the moment, hence the library is used.\n",
"\n",
"We start by defining a portfolio and an acute vulnerability function for Wind."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# pip install nbformat pandas plotly requests"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from io import StringIO\n",
"import json\n",
"from typing import NamedTuple\n",
"from dependency_injector import providers\n",
"from physrisk.container import Container\n",
"from physrisk.vulnerability_models.vulnerability import VulnerabilityModelsFactory\n",
"from physrisk.vulnerability_models.config_based_impact_curves import (\n",
" VulnerabilityConfigItem,\n",
" config_items_from_csv,\n",
" config_items_to_df,\n",
")\n",
"import plotly.io\n",
"from plotly.subplots import make_subplots\n",
"\n",
"plotly.io.renderers.default = \"notebook\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define a portfolio. The \"type\" attribute can be used to define an arbitrary scheme to capture categories of assets, matching the vulnerability configuration. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"portfolio = {\n",
" \"items\": [\n",
" {\n",
" \"asset_class\": \"RealEstateAsset\",\n",
" \"type\": \"Buildings/Industrial\",\n",
" \"location\": \"Asia\",\n",
" \"latitude\": 24.0426,\n",
" \"longitude\": 91.0158,\n",
" },\n",
" {\n",
" \"asset_class\": \"RealEstateAsset\",\n",
" \"type\": \"Buildings/Industrial\",\n",
" \"location\": \"Asia\",\n",
" \"latitude\": 22.6588,\n",
" \"longitude\": 90.3373,\n",
" },\n",
" ]\n",
"}\n",
"request = {\n",
" \"assets\": portfolio,\n",
" \"include_asset_level\": True,\n",
" \"include_calc_details\": True,\n",
" \"include_measures\": True,\n",
" \"years\": [2050],\n",
" \"scenario\": \"ssp585\",\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Configuration can be defined programmatically, but is also intended to be maintained in CSV format. Here we write the config line to CSV and read back again. In practise, a file of config lines can be maintained. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hazard_class,asset_class,asset_identifier,indicator_id,indicator_units,impact_id,impact_units,curve_type,points_x,points_y,points_z,cap_of_points_x,cap_of_points_y,activation_of_points_x\n",
"Wind,RealEstateAsset,\"type=Buildings/Industrial,location=Asia\",max_speed,m/s,damage,,indicator/piecewise_linear,\"[20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 55.0, 60.0, 65.0, 70.0, 75.0, 80.0, 85.0, 90.0, 110.0]\",\"[0.0, 0.0, 0.0, 0.03, 0.11, 0.23, 0.37, 0.52, 0.63, 0.71, 0.78, 0.83, 0.87, 0.89, 0.92, 1.0]\",,,,\n",
"\n"
]
}
],
"source": [
"config_items = [\n",
" VulnerabilityConfigItem(\n",
" hazard_class=\"Wind\",\n",
" asset_class=\"RealEstateAsset\",\n",
" asset_identifier=\"type=Buildings/Industrial,location=Asia\",\n",
" indicator_id=\"max_speed\",\n",
" indicator_units=\"m/s\",\n",
" impact_id=\"damage\",\n",
" impact_units=None,\n",
" curve_type=\"indicator/piecewise_linear\",\n",
" points_x=[20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 110],\n",
" points_y=[\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0.03,\n",
" 0.11,\n",
" 0.23,\n",
" 0.37,\n",
" 0.52,\n",
" 0.63,\n",
" 0.71,\n",
" 0.78,\n",
" 0.83,\n",
" 0.87,\n",
" 0.89,\n",
" 0.92,\n",
" 1.0,\n",
" ],\n",
" activation_of_points_x=None,\n",
" cap_of_points_x=None,\n",
" cap_of_points_y=None,\n",
" )\n",
"]\n",
"\n",
"csv_config = config_items_to_df(config_items).to_csv(index=False)\n",
"print(csv_config)\n",
"\n",
"config_items = config_items_from_csv(StringIO(csv_config))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Having defined the configuration the calculation is run. Dependency injection is used to apply the custom configuration. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# we make use of dependency injection to inject a vulnerability model that accepts the configuration.\n",
"container = Container()\n",
"container.override_providers(\n",
" vulnerability_models_factory=providers.Factory(\n",
" VulnerabilityModelsFactory, config=config_items\n",
" )\n",
")\n",
"requester = container.requester()\n",
"response = json.loads(\n",
" requester.get(request_id=\"get_asset_impact\", request_dict=request)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
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"source": [
"asset0_impacts = response[\"asset_impacts\"][1][\"impacts\"]\n",
"\n",
"\n",
"class Key(NamedTuple):\n",
" hazard_type: str\n",
" scenario_id: str\n",
" year: str\n",
"\n",
"\n",
"asset0_impact_dict = {}\n",
"for i in asset0_impacts:\n",
" key = i[\"key\"]\n",
" asset0_impact_dict[Key(key[\"hazard_type\"], key[\"scenario_id\"], key[\"year\"])] = i\n",
"\n",
"hazard_types = set(k.hazard_type for k in asset0_impact_dict.keys())\n",
"wind_impact_histo = asset0_impact_dict[Key(\"Wind\", \"historical\", \"None\")]\n",
"wind_impact_ssp585 = asset0_impact_dict[Key(\"Wind\", \"ssp585\", \"2050\")]\n",
"\n",
"exceedance_histo = wind_impact_histo[\"impact_exceedance\"]\n",
"exceedance_ssp585 = wind_impact_ssp585[\"impact_exceedance\"]\n",
"\n",
"fig1 = make_subplots(rows=1, cols=1)\n",
"\n",
"fig1.add_scatter(\n",
" x=exceedance_histo[\"exceed_probabilities\"],\n",
" y=exceedance_histo[\"values\"],\n",
" name=\"baseline wind\",\n",
" row=1,\n",
" col=1,\n",
")\n",
"fig1.add_scatter(\n",
" x=exceedance_ssp585[\"exceed_probabilities\"],\n",
" y=exceedance_ssp585[\"values\"],\n",
" name=\"wind SSP585\",\n",
" row=1,\n",
" col=1,\n",
")\n",
"fig1.update_xaxes(\n",
" title=\"Annual exceedance probability\",\n",
" title_font={\"size\": 14},\n",
" row=1,\n",
" col=1,\n",
" type=\"log\",\n",
" autorange=\"reversed\",\n",
")\n",
"fig1.update_yaxes(\n",
" title=\"Damage as fraction of insurable value\", title_font={\"size\": 14}, row=1, col=1\n",
")"
]
}
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