Refactoring to support risk models; resync inventory

methodology/PhysicalRiskMethodologyBibliography.bib

@@ -70,7 +70,7 @@ @inbook{Cooley:2013
 @techreport{CFRF:2022,
   title={Scenario Analysis: Physical Risk},
   institution={Climate Financial Risk Forum},
-  url={ttps://www.fca.org.uk/publication/corporate/cfrf-guide-2022-scenario-analysis-physical-risk-underwriting-guide.pdf.pdf},
+  url={https://www.fca.org.uk/publication/corporate/cfrf-guide-2022-scenario-analysis-physical-risk-underwriting-guide.pdf.pdf},
   year={2022}
 }
 

src/physrisk/__init__.py

@@ -1,9 +0,0 @@
-from .kernel import (
-    Asset,
-    Drought,
-    ExceedanceCurve,
-    HazardEventDistrib,
-    RiverineInundation,
-    VulnerabilityDistrib,
-    calculate_impacts,
-)

src/physrisk/api/v1/hazard_data.py

@@ -30,15 +30,21 @@ class MapInfo(BaseModel):
     """Provides information about map layer."""
 
     colormap: Optional[Colormap] = Field(description="Details of colormap.")
-    array_name: Optional[str] = Field(
-        description="Name of array reprojected to Web Mercator for on-the-fly display or to hash to obtain tile ID. If not supplied, convention is to add '_map' to array_name."  # noqa
+    path: str = Field(
+        description="Name of array reprojected to Web Mercator for on-the-fly display or to hash to obtain tile ID. If not supplied, convention is to add '_map' to path."  # noqa
     )
-    bounds: Optional[List[Tuple[float, float]]] = Field(
+    bounds: List[Tuple[float, float]] = Field(
         [(-180.0, 85.0), (180.0, 85.0), (180.0, -85.0), (-180.0, -85.0)],
         description="Bounds (top/left, top/right, bottom/right, bottom/left) as degrees. Note applied to map reprojected into Web Mercator CRS.",  # noqa
     )
     # note that the bounds should be consistent with the array attributes
-    source: Optional[str] = Field(description="Source of map image: 'map_array' or 'tiles'.")
+    source: Optional[str] = Field(
+        description="""Source of map image. These are
+                            'map_array': single Mercator projection array at path above
+                            'map_array_pyramid': pyramid of Mercator projection arrays
+                            'mapbox'.
+                                  """
+    )
 
 
 class Period(BaseModel):
@@ -69,6 +75,11 @@ class HazardResource(BaseModel):
     indicator_id: str = Field(
         description="Identifier of the hazard indicator (i.e. the modelled quantity), e.g. 'flood_depth'."
     )
+    indicator_model_id: Optional[str] = Field(
+        None,
+        description="Identifier specifying the type of model used in the derivation of the indicator "
+        "(e.g. whether flood model includes impact of sea-level rise).",
+    )
     indicator_model_gcm: str = Field(
         description="Identifier of general circulation model(s) used in the derivation of the indicator."
     )
@@ -119,11 +130,7 @@ def expand_resource(
                         else (
                             item.map.copy(
                                 deep=True,
-                                update={
-                                    "array_name": expand(
-                                        item.map.array_name if item.map.array_name is not None else "", key, param
-                                    )
-                                },
+                                update={"path": expand(item.map.path if item.map.path is not None else "", key, param)},
                             )
                         ),
                     },

src/physrisk/api/v1/hazard_image.py

@@ -1,9 +1,20 @@
-from typing import Optional
+from typing import NamedTuple, Optional
 
 from pydantic import BaseModel, Field
 
 from physrisk.api.v1.common import BaseHazardRequest
 
+# class Tile(BaseHazardRequest):
+#     x: int
+#     y: int
+#     z: int
+
+
+class Tile(NamedTuple):
+    x: int
+    y: int
+    z: int
+
 
 class HazardImageRequest(BaseHazardRequest):
     resource: str = Field(description="Full path to the array; formed by '{path}/{id}'.")
@@ -13,6 +24,7 @@ class HazardImageRequest(BaseHazardRequest):
     format: Optional[str] = Field("PNG")
     min_value: Optional[float]
     max_value: Optional[float]
+    tile: Optional[Tile]
 
 
 class HazardImageResponse(BaseModel):

src/physrisk/api/v1/impact_req_resp.py

@@ -1,11 +1,10 @@
+from enum import Enum
 from typing import List, Optional
 
 from pydantic import BaseModel, Field
 
 from physrisk.api.v1.common import Assets, Distribution, ExceedanceCurve, VulnerabilityDistrib
 
-# region Request
-
 
 class CalcSettings(BaseModel):
     hazard_interp: str = Field("floor", description="Method used for interpolation of hazards: 'floor' or 'bilinear'.")
@@ -16,7 +15,8 @@ class AssetImpactRequest(BaseModel):
 
     assets: Assets
     calc_settings: CalcSettings = Field(default_factory=CalcSettings, description="Interpolation method.")
-    include_asset_level: bool = Field(True, description="If true, include ")
+    include_asset_level: bool = Field(True, description="If true, include asset-level impacts.")
+    include_measures: bool = Field(True, description="If true, include measures.")
     include_calc_details: bool = Field(True, description="If true, include impact calculation details.")
     scenario: str = Field("rcp8p5", description="Name of scenario ('rcp8p5')")
     year: int = Field(
@@ -25,11 +25,34 @@ class AssetImpactRequest(BaseModel):
     )
 
 
-# endregion
-
 # region Response
 
 
+class Category(str, Enum):
+    NODATA = "NODATA"
+    LOW = "LOW"
+    MEDIUM = "MEDIUM"
+    HIGH = "HIGH"
+    REDFLAG = "REDFLAG"
+
+
+class Indicator(BaseModel):
+    value: float
+    label: str
+
+
+class RiskMeasureResult(BaseModel):
+    """Provides a risk category based on one or more risk indicators.
+    A risk indicator is a quantity derived from one or more vulnerability models,
+    e.g. the change in 1-in-100 year damage or disruption.
+    """
+
+    category: Category = Field(description="Result category.")
+    cat_defn: str = Field(description="Definition of the category for the particular indicator.")
+    indicators: List[Indicator]
+    summary: str = Field(description="Summary of the indicator.")
+
+
 class AcuteHazardCalculationDetails(BaseModel):
     """Details of an acute hazard calculation."""
 
@@ -48,6 +71,7 @@ class AssetSingleHazardImpact(BaseModel):
         ('damage') or disruption to the annual economic benefit obtained from the asset ('disruption'), expressed as
         fractional decrease to an equivalent cash amount.""",
     )
+    risk_measure: Optional[RiskMeasureResult]
     impact_distribution: Distribution
     impact_exceedance: ExceedanceCurve
     impact_mean: float

src/physrisk/data/image_creator.py

@@ -1,5 +1,7 @@
 import io
-from typing import Callable, List, Optional
+from functools import lru_cache
+from pathlib import PurePosixPath
+from typing import Callable, List, NamedTuple, Optional
 
 import numpy as np
 import PIL.Image as Image
@@ -9,6 +11,12 @@
 from physrisk.data.zarr_reader import ZarrReader
 
 
+class Tile(NamedTuple):
+    x: int
+    y: int
+    z: int
+
+
 class ImageCreator:
     """Convert small arrays into images for map display.
     Intended for arrays <~1500x1500 (otherwise, recommended to use Mapbox tiles - or similar).
@@ -22,6 +30,7 @@ def convert(
         path: str,
         format="PNG",
         colormap: str = "heating",
+        tile: Optional[Tile] = None,
         min_value: Optional[float] = None,
         max_value: Optional[float] = None,
     ) -> bytes:
@@ -37,7 +46,7 @@ def convert(
         Returns:
             bytes: Image data.
         """
-        image = self._to_image(path, colormap, min_value=min_value, max_value=max_value)
+        image = self._to_image(path, colormap, tile=tile, min_value=min_value, max_value=max_value)
         image_bytes = io.BytesIO()
         image.save(image_bytes, format=format)
         return image_bytes.getvalue()
@@ -65,12 +74,27 @@ def to_file(
         image.save(filename, format=format)
 
     def _to_image(
-        self, path, colormap: str = "heating", min_value: Optional[float] = None, max_value: Optional[float] = None
+        self,
+        path,
+        colormap: str = "heating",
+        tile: Optional[Tile] = None,
+        index: Optional[int] = None,
+        min_value: Optional[float] = None,
+        max_value: Optional[float] = None,
     ) -> Image.Image:
         """Get image for path specified as array of bytes."""
-        data = self.reader.all_data(path)
+        tile_path = path if tile is None else str(PurePosixPath(path, f"{tile.z}"))
+        data = get_data(self.reader, tile_path)
+        # data = self.reader.all_data(tile_path)
         if len(data.shape) == 3:
-            data = data[:, :, :].squeeze(axis=0)
+            index = len(self.reader.get_index_values(data)) - 1 if index is None else index
+            if tile is None:
+                # return whole array
+                data = data[index, :, :]  # .squeeze(axis=0)
+            else:
+                # (from zarr 2.16.0 we can also use block indexing)
+                data = data[index, 256 * tile.y : 256 * (tile.y + 1), 256 * tile.x : 256 * (tile.x + 1)]
+
         if any(dim > 1500 for dim in data.shape):
             raise Exception("dimension too large (over 1500).")
         map_defn = colormap_provider.colormap(colormap)
@@ -169,3 +193,8 @@ def test_store(path: str):
         )
         z[0, :, :] = im
         return store
+
+
+@lru_cache(maxsize=32)
+def get_data(reader, path):
+    return reader.all_data(path)

src/physrisk/data/inventory.py

@@ -89,14 +89,14 @@ def expand(resources: List[HazardResource]) -> List[HazardResource]:
     expanded_models = [e for model in resources for e in model.expand()]
     # we populate map_id hashes programmatically
     for model in expanded_models:
-        if model.map and model.map.source == "mapbox" and model.map.array_name:
+        if model.map and model.map.source == "mapbox" and model.map.path:
             for scenario in model.scenarios:
                 test_periods = scenario.periods
                 scenario.periods = []
                 for year in scenario.years:
-                    name_format = model.map.array_name
-                    array_name = name_format.format(scenario=scenario.id, year=year, return_period=1000)
-                    id = alphanumeric(array_name)[0:6]
+                    name_format = model.map.path
+                    path = name_format.format(scenario=scenario.id, year=year, return_period=1000)
+                    id = alphanumeric(path)[0:6]
                     scenario.periods.append(Period(year=year, map_id=id))
                 # if a period was specified explicitly, we check that hash is the same: a build-in check
                 if test_periods is not None: