refactor(distribution): now there can be several analytical calculations for one characteristic

Author: LeonidElkin

src/pysatl_core/distributions/strategies.py

@@ -11,7 +11,7 @@
 __copyright__ = "Copyright (c) 2025 PySATL project"
 __license__ = "SPDX-License-Identifier: MIT"
 
-from typing import TYPE_CHECKING, Any, Protocol, cast
+from typing import TYPE_CHECKING, Protocol, cast
 
 import numpy as np
 
@@ -20,13 +20,14 @@
 
 if TYPE_CHECKING:
     from collections.abc import Mapping
+    from typing import Any
 
     from pysatl_core.distributions.computation import (
         AnalyticalComputation,
         FittedComputationMethod,
+        Method,
     )
     from pysatl_core.distributions.distribution import Distribution
-    from pysatl_core.distributions.registry.graph import RegistryView
     from pysatl_core.types import GenericCharacteristicName, LabelName
 
 
@@ -125,30 +126,16 @@ def _pick_analytical_method(
                 f"Characteristic '{state}' provides no labeled analytical computations."
             ) from exc
 
-    @staticmethod
-    def _pick_analytical_loop_method(
-        state: GenericCharacteristicName,
-        view: RegistryView,
-    ) -> Method[Any, Any] | None:
-        """
-        Pick the first analytical self-loop method for a characteristic in a view.
-        """
-        loops = view.analytical_variants(state)
-        if not loops:
-            return None
-        return cast(Method[Any, Any], next(iter(loops.values())).method)
-
     def query_method(
         self, state: GenericCharacteristicName, distr: Distribution, **options: Any
     ) -> Method[Any, Any]:
         """
         Resolve a computation method for the target characteristic.
 
         Resolution order:
-        1. Cached fitted method (if caching enabled)
-        2. Analytical implementation for non-registry characteristics
-        3. Analytical self-loop from the registry view
-        4. Conversion path from analytical-loop characteristics via the graph
+        1. Analytical implementation from the distribution
+        2. Cached fitted method (if caching enabled)
+        3. Conversion path from an analytical characteristic via the graph
 
         Parameters
         ----------
@@ -170,46 +157,34 @@ def query_method(
             If no analytical base exists, no conversion path is found,
             or a cycle is detected.
         """
-        # 1. Check cache if enabled
+        # 1. Check for analytical implementation
+        if state in distr.analytical_computations:
+            return self._pick_analytical_method(state, distr.analytical_computations[state])
+
+        # 2. Check cache if enabled
         if self._enable_caching:
             cached = self._cache.get(state)
             if cached is not None:
                 return cached
 
-        # 2. Require at least one analytical characteristic
+        # 3. Require at least one analytical characteristic
         if not distr.analytical_computations:
             raise RuntimeError(
                 "Distribution provides no analytical computations to ground conversions."
             )
 
-        # 3. Non-registry characteristics are resolved directly.
-        # It covers the situation where user is providing their analytical computation which isn't
-        # in the graph
-        registry = characteristic_registry()
-        if state not in registry.declared_characteristics:
-            if state in distr.analytical_computations:
-                return self._pick_analytical_method(state, distr.analytical_computations[state])
-            raise RuntimeError(
-                f"Characteristic '{state}' is not declared in the registry and has no "
-                "analytical implementation in the distribution."
-            )
-
-        # 4. Get filtered graph view for this distribution.
-        view = registry.view(distr)
+        # 4. Get filtered graph view for this distribution
+        reg = characteristic_registry().view(distr)
 
         self._push_guard(distr, state)
         try:
-            loop_method = self._pick_analytical_loop_method(state, view)
-            if loop_method is not None:
-                return loop_method
-
-            # 5. Try each analytical-loop characteristic as a source
+            # 5. Try each analytical characteristic as a source
             for src in distr.analytical_computations:
-                if not view.analytical_variants(src):
-                    continue
+                if src == state:
+                    return self._pick_analytical_method(src, distr.analytical_computations[src])
 
                 # Find conversion path in the graph
-                path = view.find_path(src, state)
+                path = reg.find_path(src, state)
                 if not path:
                     continue