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Add test for the commercial real estate model (#149)
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* Add test for the commercial real estate model

Add a test for the commercial real estate model;
Fix some typos, formatting and warnings;
Copy latest version of notebooks/vulnerability_onboarding/EU JRC global flood depth-damage functions/EU JRC global flood depth-damage functions.json to src\physrisk\data\static\vulnerability.

Signed-off-by: EglantineGiraud <[email protected]>
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EglantineGiraud authored Sep 30, 2023
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29 changes: 17 additions & 12 deletions docs/handbook/onboarding.rst
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Expand Up @@ -19,38 +19,43 @@ Probability bins can be obtained from the exceedance curve, by subtracting one c

Note that in this case - which is the standard case - linear interpolation between points of the exceedance curve is assumed which corresponds to flat probability density within a bin.

In code this can be done using the :code:`ExceedanceCurve`:
::
exceedance_curve = ExceedanceCurve(1.0 / return_periods, event_intensities)
intensity_bins, probs = exceedance_curve.get_probability_bins()
Vulnerability/Event Model
In code, this can be done using the :code:`ExceedanceCurve`:

.. code-block::
exceedance_curve = ExceedanceCurve(1.0 / return_periods, event_intensities)
intensity_bins, probs = exceedance_curve.get_probability_bins()
Vulnerability/Event Model
-------------------------
In general a Vulnerability/Event Model is responsible for obtaining for a particular asset instances of:
In general, a Vulnerability/Event Model is responsible for obtaining for a particular asset instances of:

#. :code:`HazardEventDistrib` that provides probabilities of hazard event intensentities for the asset, and
#. :code:`HazardEventDistrib` that provides probabilities of hazard event intensities for the asset, and
#. :code:`VulnerabilityDistrib` that provides conditional probabilities that given a hazard event of given intensity has occurred, a damage/disruption of a given level will occur.

The damage or disruption is sometimes referred to as the 'impact'.

The current implementation is non-parametric and based on discete bins - although continuous versions of :code:`HazardEventDistrib`/:code:`VulnerabilityDistrib` could certainly be added, based on particular parametric distributions.
The current implementation is non-parametric and based on discrete bins - although continuous versions of :code:`HazardEventDistrib`/:code:`VulnerabilityDistrib` could certainly be added, based on particular parametric distributions.

:code:`HazardEventDistrib` is in this non-parametric version a histogram of hazard event intensities: defines a set of intensity bins and the annual probability of occurrence.

:code:`VulnerabilityDistrib` is a matix that provides the probability that if an event occurs with intensity falling in a particular intensity bin, an impact in a particular impact bin occurs.
:code:`VulnerabilityDistrib` is a matrix that provides the probability that if an event occurs with intensity falling in a particular intensity bin, an impact in a particular impact bin occurs.

The Vulnerability/Event Model (henceforth 'model') is in general responsible for

* Defining its hazard event data requirements by implementing method :code:`get_event_data_requests`
* Using the data to construct instances of :code:`HazardEventDistrib` and :code:`VulnerabilityDistrib` that will be used in the impact calculation. This is done by implementing method :code:`get_distributions`

:code:`HazardEventDistrib` and :code:`VulnerabilityDistrib` can be constructed in a single method to ensure their alignment, although this is method is probably only required in most bespoke cases. :code:`get_event_data_requests` is done as a separate step for performance reasons: it is desirable that all models state their data requirements 'up-front' in order that requests can be batched for fast retrieval.
:code:`HazardEventDistrib` and :code:`VulnerabilityDistrib` can be constructed in a single method to ensure their alignment, although this method is probably only required in most bespoke cases. :code:`get_event_data_requests` is done as a separate step for performance reasons: it is desirable that all models state their data requirements 'up-front' in order that requests can be batched for fast retrieval.

The model applies to:

* A type of hazard event (Inundation, Wildfire, Drought, etc)
* A type of asset (residential property, power generating asset, etc)

Decorators are used to 'tag' a model, so that an appropriate model can be selected for a given asset and hazard type; configuration allows types of :code:`Model` to be used in preference to other candidates.

Specific types of model also exist for common modelling approaches. In particular, although in general it is desirable that a model has the flexibility to define its hazard event distribution and vulnerability distribution, in many cases the former will be sourced directly from a data set and it only remains to define the vulnerability distribution. The :code:`Model` class allows the general form of the Model to be implemented. The :code:`VulnerabilityModel` class is for cases where only the vulnerability is to be specified.
Specific types of model also exist for common modelling approaches. In particular, although in general it is desirable that a model has the flexibility to define its hazard event distribution and vulnerability distribution, in many cases the former will be sourced directly from a data set and it only remains to define the vulnerability distribution. The :code:`Model` class allows the general form of the model to be implemented. The :code:`VulnerabilityModel` class is for cases where only the vulnerability is to be specified.

On-boarding a model based on a damage/disruption curve
------------------------------------------------------
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193 changes: 174 additions & 19 deletions src/physrisk/data/static/vulnerability/EU JRC global flood depth-damage functions.json
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Expand Up @@ -256,47 +256,142 @@
{
"asset_type": "Buildings/Commercial",
"event_type": "RiverineInundation",
"impact_mean": [],
"impact_mean": [
0.0,
0.15,
0.3,
0.45,
0.55,
0.75,
0.9,
1.0,
1.0
],
"impact_std": [],
"impact_type": "Damage",
"intensity": [],
"intensity": [
0.0,
0.5,
1.0,
1.5,
2.0,
3.0,
4.0,
5.0,
6.0
],
"intensity_units": "m",
"location": "Europe"
},
{
"asset_type": "Buildings/Commercial",
"event_type": "RiverineInundation",
"impact_mean": [
0.0
],
"impact_std": [
0.0
0.0,
0.018404908,
0.239263804,
0.374233129,
0.466257669,
0.552147239,
0.687116564,
0.82208589,
0.90797546,
1.0
],
"impact_std": [],
"impact_type": "Damage",
"intensity": [
0.0,
0.01
0.01,
0.5,
1.0,
1.5,
2.0,
3.0,
4.0,
5.0,
6.0
],
"intensity_units": "m",
"location": "North America"
},
{
"asset_type": "Buildings/Commercial",
"event_type": "RiverineInundation",
"impact_mean": [],
"impact_std": [],
"impact_mean": [
0.0,
0.611477587,
0.839531094,
0.923588457,
0.991972477,
1.0,
1.0,
1.0,
1.0
],
"impact_std": [
0.0,
0.077023435,
0.035924027,
0.026876525,
0.016055046,
0.0,
0.0,
0.0,
0.0
],
"impact_type": "Damage",
"intensity": [],
"intensity": [
0.0,
0.5,
1.0,
1.5,
2.0,
3.0,
4.0,
5.0,
6.0
],
"intensity_units": "m",
"location": "South America"
},
{
"asset_type": "Buildings/Commercial",
"event_type": "RiverineInundation",
"impact_mean": [],
"impact_std": [],
"impact_mean": [
0.0,
0.376789623,
0.537681619,
0.659336684,
0.762845232,
0.883348656,
0.941854895,
0.98075938,
1.0
],
"impact_std": [
0.0,
0.240462285,
0.240596279,
0.243605156,
0.250253511,
0.171703625,
0.11240992,
0.052781064,
0.0
],
"impact_type": "Damage",
"intensity": [],
"intensity": [
0.0,
0.5,
1.0,
1.5,
2.0,
3.0,
4.0,
5.0,
6.0
],
"intensity_units": "m",
"location": "Asia"
},
Expand All @@ -306,27 +401,87 @@
"impact_mean": [],
"impact_std": [],
"impact_type": "Damage",
"intensity": [],
"intensity": [
0.0,
0.5,
1.0,
1.5,
2.0,
3.0,
4.0,
5.0,
6.0
],
"intensity_units": "m",
"location": "Africa"
},
{
"asset_type": "Buildings/Commercial",
"event_type": "RiverineInundation",
"impact_mean": [],
"impact_std": [],
"impact_mean": [
0.0,
0.238953575,
0.481199682,
0.673795091,
0.864583333,
1.0,
1.0,
1.0,
1.0
],
"impact_std": [
0.0,
0.142878204,
0.204113206,
0.190903594,
0.178000078,
0.0,
0.0,
0.0,
0.0
],
"impact_type": "Damage",
"intensity": [],
"intensity": [
0.0,
0.5,
1.0,
1.5,
2.0,
3.0,
4.0,
5.0,
6.0
],
"intensity_units": "m",
"location": "Oceania"
},
{
"asset_type": "Buildings/Commercial",
"event_type": "RiverineInundation",
"impact_mean": [],
"impact_mean": [
0.0,
0.323296918,
0.506529105,
0.63459558,
0.744309656,
0.864093044,
0.932788157,
0.977746968,
1.0
],
"impact_std": [],
"impact_type": "Damage",
"intensity": [],
"intensity": [
0.0,
0.5,
1.0,
1.5,
2.0,
3.0,
4.0,
5.0,
6.0
],
"intensity_units": "m",
"location": "Global"
},
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2 changes: 1 addition & 1 deletion src/test/models/test_power_generating_asset_models.py
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Expand Up @@ -21,7 +21,7 @@
class TestPowerGeneratingAssetModels(TestWithCredentials):
"""Tests World Resource Institute (WRI) models for power generating assets."""

def test_innundation(self):
def test_inundation(self):
# exceedance curve
return_periods = np.array([2.0, 5.0, 10.0, 25.0, 50.0, 100.0, 250.0, 500.0, 1000.0])
base_depth = np.array(
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