Tipping points

flood_adapt/dbs_controller.py

@@ -58,9 +58,9 @@ def __init__(
             The name of the database.
         Notes
         """
-        self.input_path = Path(database_path / database_name / "input")
-        self.static_path = Path(database_path / database_name / "static")
-        self.output_path = Path(database_path / database_name / "output")
+        self.input_path = Path(database_path) / database_name / "input"
+        self.static_path = Path(database_path) / database_name / "static"
+        self.output_path = Path(database_path) / database_name / "output"
 
         self.site = Site.load_file(self.static_path / "site" / "site.toml")
         self.aggr_areas = self.get_aggregation_areas()

flood_adapt/object_model/benefit.py

@@ -504,7 +504,7 @@ def load_file(filepath: Union[str, os.PathLike]):
         obj = Benefit()
         with open(filepath, mode="rb") as fp:
             toml = tomli.load(fp)
-        obj.attrs = BenefitModel.parse_obj(toml)
+        obj.attrs = BenefitModel.model_validate(toml)  # Replace parse_obj with model_validate
         # if benefits is created by path use that to get to the database path
         obj.database_input_path = Path(filepath).parents[2]
         obj.init()
@@ -515,12 +515,12 @@ def load_dict(data: dict[str, Any], database_input_path: Union[str, os.PathLike]
         """create Benefit object from dictionary, e.g. when initialized from GUI"""
 
         obj = Benefit()
-        obj.attrs = BenefitModel.parse_obj(data)
+        obj.attrs = BenefitModel.model_validate(data)
         obj.database_input_path = Path(database_input_path)
         obj.init()
         return obj
 
     def save(self, filepath: Union[str, os.PathLike]):
         """save Benefit object to a toml file"""
         with open(filepath, "wb") as f:
-            tomli_w.dump(self.attrs.dict(exclude_none=True), f)
+            tomli_w.model_dump(self.attrs, f, exclude_none=True)

flood_adapt/object_model/interface/tipping_points.py

@@ -0,0 +1,101 @@
+import os
+from abc import ABC, abstractmethod
+from typing import Any, Optional, Union
+from enum import Enum
+
+import pandas as pd
+from pydantic import BaseModel
+
+
+class TippingPointMetrics(str, Enum):
+    """class describing the accepted input for the variable metric_type in TippingPoint"""
+
+    # based on what I have found in floodadapt - but can be changed
+    FloodedAll = "FloodedAll"
+    FloodedLowVulnerability = "FloodedLowVulnerability"
+    FloodedHighVulnerability = "FloodedHighVulnerability"
+    TotalDamageEvent = "TotalDamageEvent"
+    TotalResDamageEvent = "TotalResDamageEvent"
+    ResidentialMinorCount = "ResidentialMinorCount"
+    ResidentialMajorCount = "ResidentialMajorCount"
+    ResidentialDestroyedCount = "ResidentialDestroyedCount"
+    CommercialCount = "CommercialCount"
+    CommercialMinorCount = "CommercialMinorCount"
+    CommercialMajorCount = "CommercialMajorCount"
+    CommercialDestroyedCount = "CommercialDestroyedCount"
+    HealthCount = "HealthCount"
+    HealthMinorCount = "HealthMinorCount"
+    HealthMajorCount = "HealthMajorCount"
+    HealthDestroyedCount = "HealthDestroyedCount"
+    SchoolsCount = "SchoolsCount"
+    SchoolsMinorCount = "SchoolsMinorCount"
+    SchoolsMajorCount = "SchoolsMajorCount"
+    SchoolsDestroyedCount = "SchoolsDestroyedCount"
+    EmergencyCount = "EmergencyCount"
+    EmergencyMinorCount = "EmergencyMinorCount"
+    EmergencyMajorCount = "EmergencyMajorCount"
+    EmergencyDestroyedCount = "EmergencyDestroyedCount"
+    DisplacedLowVulnerability = "DisplacedLowVulnerability"
+    DisplacedHighVulnerability = "DisplacedHighVulnerability"
+    SlightlyFloodedRoads = "SlightlyFloodedRoads"
+    MinorFloodedRoads = "MinorFloodedRoads"
+    MajorFloodedRoads = "MajorFloodedRoads"
+    FullyFloodedRoads = "FullyFloodedRoads"
+
+
+class TippingPointStatus(str, Enum):
+    """class describing the accepted input for the variable metric_type in TippingPoint"""
+
+    reached = "reached"
+    not_reached = "not_reached"
+    completed = "completed"
+
+
+class TippingPointOperator(str, Enum):
+    """class describing the accepted input for the variable operator in TippingPoint"""
+
+    greater = "greater"
+    less = "less"
+
+
+class TipPointModel(BaseModel):
+    """BaseModel describing the expected variables and data types of a Tipping Point analysis object"""
+
+    name: str
+    description: Optional[str] = ""
+    strategy: str
+    event_set: str
+    projection: str
+    sealevelrise: list[float]  # could be a numpy array too
+    tipping_point_metric: list[tuple[TippingPointMetrics, float, TippingPointOperator]]
+    status: Optional[TippingPointStatus] = TippingPointStatus.not_reached
+
+
+class ITipPoint(ABC):
+    attrs: TipPointModel
+    database_input_path: Union[str, os.PathLike]
+    results_path: Union[str, os.PathLike]
+    scenarios: pd.DataFrame
+    has_run: bool = False
+
+    @staticmethod
+    @abstractmethod
+    def load_file(filepath: Union[str, os.PathLike]):
+        """get Tipping Point attributes from toml file"""
+        ...
+
+    @staticmethod  # copping from benefits.py
+    @abstractmethod
+    def load_dict(data: dict[str, Any]):
+        """get Tipping Point attributes from an object, e.g. when initialized from GUI"""
+        ...
+
+    @abstractmethod
+    def save(self, filepath: Union[str, os.PathLike]):
+        """save Tipping Point attributes to a toml file"""
+        ...
+
+    @abstractmethod
+    def check_scenarios(self) -> pd.DataFrame:
+        """Check which scenarios are needed for this tipping point calculation and if they have already been created"""
+        ...