Added model and services

source/package/adaptation_pathways/model/action.py

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+from comparisons import SequenceComparison
+from metric import Metric, MetricValue
+
+class Action:
+    id: str
+    name: str
+    color: str
+    icon: str
+    metric_data: dict[Metric, MetricValue | None]
+
+class ActionDependency:
+    id: str
+    action: Action
+    relation: SequenceComparison
+    other_actions: list[Action]
+    actions_in_order: bool

source/package/adaptation_pathways/model/comparisons.py

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+from enum import Enum
+
+class SequenceComparison(Enum):
+    STARTS_WITH = 1
+    DOESNT_START_WITH = 2
+    CONTAINS = 3
+    DOESNT_CONTAIN = 4
+    ENDS_WITH = 5
+    DOESNT_END_WITH = 6
+
+class NumberComparison(Enum):
+    EQUAL = 1
+    DOESNT_EQUAL = 2
+    LESS_THAN = 3
+    LESS_THAN_OR_EQUAL = 4
+    GREATER_THAN = 5
+    GREATER_THAN_OR_EQUAL = 6

source/package/adaptation_pathways/model/filter.py

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+from comparisons import SequenceComparison, NumberComparison
+from metric import Metric
+from action import Action
+
+class ActionFilter:
+    relation: SequenceComparison
+    actions: list[Action]
+    actions_in_order: bool
+
+class MetricFilter:
+    metric: Metric
+    relation: NumberComparison
+    value: float

source/package/adaptation_pathways/model/metric.py

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+from enum import Enum
+
+class MetricEstimate(Enum):
+    MANUAL = 1
+    SUM = 2
+    AVERAGE = 3
+    MINIMUM = 4
+    MAXIMUM = 5
+    LAST = 6
+
+class MetricUnit:
+    symbol: str
+    place_after_value: bool
+    value_format: str
+
+class Metric:
+    id: str
+    name: str
+    unit: MetricUnit
+    current_value: float
+    estimate: MetricEstimate
+
+class MetricValue:
+    value: float
+    is_estimate: bool

source/package/adaptation_pathways/model/pathway.py

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+from metric import Metric, MetricValue
+from action import Action
+
+class Pathway:
+    id: str
+    actions: list[Action]
+    metric_data: dict[Metric, MetricValue | None]
+

source/package/adaptation_pathways/model/pathways_project.py

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+from metric import Metric
+from action import Action, ActionDependency
+from pathway import Pathway
+from scenario import Scenario
+
+class PathwaysProject:
+    id: str
+    name: str
+    organization: str
+    start_year: int
+    end_year: int
+    conditions: list[Metric]
+    criteria: list[Metric]
+    actions: list[Action]
+    action_dependencies: list[ActionDependency]
+    pathways: list[Pathway]
+    scenarios: list[Scenario]

source/package/adaptation_pathways/model/scenario.py

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+from metric import Metric, MetricValue
+
+class TimeSeriesPoint:
+    time: float
+    data: MetricValue | None
+
+class Scenario:
+    id: str
+    name: str
+    metric_data_over_time: dict[Metric, list[TimeSeriesPoint]]

source/package/adaptation_pathways/services/pathway_service.py

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+from model.filter import ActionFilter, MetricFilter
+from model.pathway import Pathway
+from model.action import Action
+from model.metric import Metric, MetricEstimate
+
+class PathwayService:
+    def filter_pathways(
+            all_pathways: list[Pathway], 
+            action_filters: list[ActionFilter], 
+            metric_filters: list[MetricFilter]
+    ) -> list[Pathway]:
+        # Replace with actual filtering code
+        return all_pathways
+
+    def generate_pathways(
+            all_actions: list[Action],
+            all_metrics: list[Metric],
+            action_constraints: list[ActionFilter],
+            metric_constraints: list[MetricFilter],
+            max_sequence_length: int | None = None
+    ) -> list[Pathway]:
+        # Generate all pathways that don't violate the provided constraints
+        # Estimate a value for each metric based on its Metric.estimate method
+        return []
+
+    def estimate_metric(
+            pathway: Pathway,
+            metric: Metric,
+            all_actions: list[Action],
+            all_pathways: list[Pathway]
+    ) -> float:        
+        # For manual estimate, just use the existing data (if there is any)
+        if metric.estimate is MetricEstimate.MANUAL:
+            current_data = pathway.metric_data.get(metric)
+            if (current_data is None or current_data.is_estimate):
+                return 0
+            else:
+                return current_data.value
+
+        # Do the proper estimate respecting the Metric.estimate method
+        return 0

source/package/adaptation_pathways/services/scenario_service.py

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+from model.scenario import Scenario
+from model.metric import Metric
+
+class ScenarioService:
+    def estimate_metric_at_time(
+            scenario: Scenario,
+            metric: Metric,
+            time: float
+    ) -> float:
+        # Replace with a linear interpolation/extrapolation using the nearest points
+        return metric.current_value