Strategies now is one per distribution

src/pysatl_core/distributions/distribution.py

@@ -11,10 +11,13 @@
 __copyright__ = "Copyright (c) 2025 PySATL project"
 __license__ = "SPDX-License-Identifier: MIT"
 
-from typing import TYPE_CHECKING, Protocol, cast, runtime_checkable
+from typing import TYPE_CHECKING, Protocol, Self, cast, runtime_checkable
 
 from pysatl_core.types import NumericArray
 
+_KEEP: object = object()
+
+
 if TYPE_CHECKING:
     from collections.abc import Mapping
     from typing import Any
@@ -71,6 +74,39 @@ def computation_strategy(self) -> ComputationStrategy: ...
     @property
     def support(self) -> Support | None: ...
 
+    def _clone_with_strategies(
+        self,
+        *,
+        sampling_strategy: SamplingStrategy | None | object = _KEEP,
+        computation_strategy: ComputationStrategy | None | object = _KEEP,
+    ) -> Distribution: ...
+
+    def with_sampling_strategy(self, sampling_strategy: SamplingStrategy | None) -> Self:
+        """Return a copy of this distribution with an updated sampling strategy."""
+        return cast(Self, self._clone_with_strategies(sampling_strategy=sampling_strategy))
+
+    def with_computation_strategy(self, computation_strategy: ComputationStrategy | None) -> Self:
+        """Return a copy of this distribution with an updated computation strategy."""
+        return cast(
+            Self,
+            self._clone_with_strategies(computation_strategy=computation_strategy),
+        )
+
+    def with_strategies(
+        self,
+        *,
+        sampling_strategy: SamplingStrategy | None | object = _KEEP,
+        computation_strategy: ComputationStrategy | None | object = _KEEP,
+    ) -> Self:
+        """Return a copy of this distribution with updated strategies."""
+        return cast(
+            Self,
+            self._clone_with_strategies(
+                sampling_strategy=sampling_strategy,
+                computation_strategy=computation_strategy,
+            ),
+        )
+
     def query_method(
         self, characteristic_name: GenericCharacteristicName, **options: Any
     ) -> Method[Any, Any]:

src/pysatl_core/families/distribution.py

@@ -11,11 +11,13 @@
 __copyright__ = "Copyright (c) 2025 PySATL project"
 __license__ = "SPDX-License-Identifier: MIT"
 
-
-from dataclasses import dataclass
 from typing import TYPE_CHECKING, cast
 
-from pysatl_core.distributions.distribution import Distribution
+from pysatl_core.distributions.distribution import _KEEP, Distribution
+from pysatl_core.distributions.strategies import (
+    DefaultComputationStrategy,
+    DefaultSamplingUnivariateStrategy,
+)
 from pysatl_core.families.registry import ParametricFamilyRegister
 from pysatl_core.types import NumericArray
 
@@ -41,7 +43,6 @@
     )
 
 
-@dataclass(slots=True)
 class ParametricFamilyDistribution(Distribution):
     """
     A specific distribution instance from a parametric family.
@@ -53,18 +54,46 @@ class ParametricFamilyDistribution(Distribution):
     ----------
     family_name : str
         Name of the distribution family.
-    _distribution_type : DistributionType
+    distribution_type : DistributionType
         Type of this distribution.
-    _parametrization : Parametrization
+    parametrization : Parametrization
         Parameter values for this distribution.
-    _support : Support or None
+    support : Support or None
         Support of this distribution.
+    sampling_strategy : SamplingStrategy
+        Strategy for generating random samples.
+        Such an object is unique for each distribution.
+    computation_strategy : ComputationStrategy
+        Strategy for computing characteristics and conversions.
+        Such an object is unique for each distribution.
     """
 
-    family_name: str
-    _distribution_type: DistributionType
-    _parametrization: Parametrization
-    _support: Support | None
+    def __init__(
+        self,
+        family_name: str,
+        distribution_type: DistributionType,
+        parametrization: Parametrization,
+        support: Support | None,
+        sampling_strategy: SamplingStrategy | None = None,
+        computation_strategy: ComputationStrategy | None = None,
+    ):
+        self._distribution_type = distribution_type
+        self._family_name = family_name
+        self._parametrization = parametrization
+        self._support = support
+
+        self._computation_strategy = computation_strategy or DefaultComputationStrategy()
+        self._sampling_strategy = sampling_strategy or DefaultSamplingUnivariateStrategy()
+
+        self._analytical_cache_key: tuple[int, GenericCharacteristicName] | None = None
+        self._analytical_cache_val: (
+            Mapping[GenericCharacteristicName, AnalyticalComputation[Any, Any]] | None
+        ) = None
+
+    @property
+    def family_name(self) -> str:
+        "Get the name of the family this distribution belongs to."
+        return self._family_name
 
     @property
     def distribution_type(self) -> DistributionType:
@@ -142,25 +171,52 @@ def analytical_computations(
         parametrization object or name changes.
         """
         key = (id(self.parametrization), self.parametrization_name)
-        cache_key = getattr(self, "_analytical_cache_key", None)
-        cache_val = getattr(self, "_analytical_cache_val", None)
 
-        if cache_key != key or cache_val is None:
-            cache_val = self.family._build_analytical_computations(self.parametrization)
+        if self._analytical_cache_key != key or self._analytical_cache_val is None:
+            self._analytical_cache_val = self.family.build_analytical_computations(
+                self.parametrization
+            )
             self._analytical_cache_key = key
-            self._analytical_cache_val = cache_val
 
-        return cache_val
+        return self._analytical_cache_val
 
     @property
     def sampling_strategy(self) -> SamplingStrategy:
         """Get the sampling strategy for this distribution."""
-        return self.family.sampling_strategy
+        return self._sampling_strategy
 
     @property
     def computation_strategy(self) -> ComputationStrategy:
         """Get the computation strategy for this distribution."""
-        return self.family.computation_strategy
+        return self._computation_strategy
+
+    def _clone_with_strategies(
+        self,
+        *,
+        sampling_strategy: SamplingStrategy | None | object = _KEEP,
+        computation_strategy: ComputationStrategy | None | object = _KEEP,
+    ) -> ParametricFamilyDistribution:
+        """Return a copy of this distribution with updated strategies."""
+        new_sampling: SamplingStrategy | None = (
+            self._sampling_strategy
+            if sampling_strategy is _KEEP
+            else cast(SamplingStrategy | None, sampling_strategy)
+        )
+
+        new_computation: ComputationStrategy | None = (
+            self._computation_strategy
+            if computation_strategy is _KEEP
+            else cast(ComputationStrategy | None, computation_strategy)
+        )
+
+        return ParametricFamilyDistribution(
+            family_name=self._family_name,
+            distribution_type=self._distribution_type,
+            parametrization=self._parametrization,
+            support=self._support,
+            sampling_strategy=new_sampling,
+            computation_strategy=new_computation,
+        )
 
     @property
     def support(self) -> Support | None:

src/pysatl_core/families/parametric_family.py

@@ -18,10 +18,6 @@
 from typing import TYPE_CHECKING, Any, cast, dataclass_transform
 
 from pysatl_core.distributions.computation import AnalyticalComputation
-from pysatl_core.distributions.strategies import (
-    DefaultComputationStrategy,
-    DefaultSamplingUnivariateStrategy,
-)
 from pysatl_core.families.distribution import ParametricFamilyDistribution
 from pysatl_core.types import ComputationFunc, DistributionType
 
@@ -83,10 +79,6 @@ class ParametricFamily:
         - pointwise characteristics (e.g., pdf, cdf, ppf): provider(params, x, **kwargs) -> Any
 
         If a single callable is provided, it is treated as defined for the base parametrization.
-    sampling_strategy : SamplingStrategy, optional
-        Strategy for sampling from distributions.
-    computation_strategy : ComputationStrategy, optional
-        Strategy for computing distribution characteristics.
     support_by_parametrization : Callable or None, optional
         Function that returns support for given parameters.
     """
@@ -97,37 +89,27 @@ def __init__(
         distr_type: DistributionType | Callable[[Parametrization], DistributionType],
         distr_parametrizations: list[ParametrizationName],
         distr_characteristics: CharacteristicsMap,
-        sampling_strategy: SamplingStrategy | None = None,
-        computation_strategy: ComputationStrategy | None = None,
         support_by_parametrization: SupportArg = None,
     ):
+        if not distr_parametrizations:
+            raise ValueError(
+                "distr_parametrizations must be non-empty (base parametrization is required)."
+            )
+
         self._name = name
+        # Ordered names; the first one is the base parametrization name
+        self.parametrization_names = distr_parametrizations
+        self.base_parametrization_name = self.parametrization_names[0]
         self._distr_type: Callable[[Parametrization], DistributionType] = (
             (lambda params: distr_type) if isinstance(distr_type, DistributionType) else distr_type
         )
 
-        self.computation_strategy = (
-            DefaultComputationStrategy() if computation_strategy is None else computation_strategy
-        )
-
-        if support_by_parametrization is None:
-            self._support_resolver: SupportResolver
-            self._support_resolver = lambda _params: None
-        else:
-            self._support_resolver = support_by_parametrization
-
-        # Ordered names; the first one is the base parametrization name
-        self.parametrization_names: list[ParametrizationName] = distr_parametrizations
-        self.base_parametrization_name: ParametrizationName = self.parametrization_names[0]
+        self._support_resolver: SupportResolver = support_by_parametrization or (lambda _p: None)
 
         # Runtime registry of parametrization classes
         self._parametrizations: dict[ParametrizationName, type[Parametrization]] = {}
 
-        self.sampling_strategy = (
-            DefaultSamplingUnivariateStrategy() if sampling_strategy is None else sampling_strategy
-        )
-
-        def _process_char_val(
+        def _normalize_characteristic(
             value: Mapping[ParametrizationName, CharacteristicFunction[Any, Any]]
             | CharacteristicFunction[Any, Any],
         ) -> dict[ParametrizationName, CharacteristicFunction[Any, Any]]:
@@ -139,21 +121,33 @@ def _process_char_val(
 
         self.distr_characteristics: dict[
             GenericCharacteristicName, dict[ParametrizationName, CharacteristicFunction[Any, Any]]
-        ] = {key: _process_char_val(val) for key, val in distr_characteristics.items()}
-
-        # Precompute analytical plan
+        ] = {k: _normalize_characteristic(v) for k, v in distr_characteristics.items()}
+
+        # Validate characteristic providers
+        valid_names = set(self.parametrization_names)
+        for char_name, forms in self.distr_characteristics.items():
+            unknown = set(forms) - valid_names
+            if unknown:
+                raise ValueError(
+                    f"Characteristic '{char_name}' has providers for unknown parametrizations: "
+                    f"{sorted(unknown)}."
+                )
+            if self.base_parametrization_name not in forms and len(forms) == 0:
+                raise ValueError(f"Characteristic '{char_name}' has no providers.")
+
+        # Precompute analytical plan: for each parametrization pick provider (self or base)
         self._analytical_plan: dict[
             ParametrizationName, dict[GenericCharacteristicName, ParametrizationName]
         ] = {}
-        base_name = self.base_parametrization_name
+        base = self.base_parametrization_name
         for pname in self.parametrization_names:
-            plan_for_p: dict[GenericCharacteristicName, ParametrizationName] = {}
+            plan: dict[GenericCharacteristicName, ParametrizationName] = {}
             for characteristic, forms in self.distr_characteristics.items():
                 if pname in forms:
-                    plan_for_p[characteristic] = pname
-                elif base_name in forms:
-                    plan_for_p[characteristic] = base_name
-            self._analytical_plan[pname] = plan_for_p
+                    plan[characteristic] = pname
+                elif base in forms:
+                    plan[characteristic] = base
+            self._analytical_plan[pname] = plan
 
     @property
     def name(self) -> str:
@@ -184,7 +178,7 @@ def base(self) -> type[Parametrization]:
 
     @property
     def support_resolver(self) -> SupportResolver:
-        """Get the support resolver function."""
+        """Support resolver callable."""
         return self._support_resolver
 
     def register_parametrization(
@@ -269,7 +263,7 @@ def _bind_parametrization[In, Out](
             else func,
         )
 
-    def _build_analytical_computations(
+    def build_analytical_computations(
         self, parameters: Parametrization
     ) -> dict[GenericCharacteristicName, AnalyticalComputation[Any, Any]]:
         """
@@ -285,8 +279,7 @@ def _build_analytical_computations(
             if provider_name == parameters.name:
                 params_obj = parameters
             else:
-                if base_params is None:
-                    base_params = self.to_base(parameters)
+                base_params = base_params or self.to_base(parameters)
                 params_obj = base_params
 
             func_factory = self.distr_characteristics[characteristic][provider_name]
@@ -299,16 +292,23 @@ def _build_analytical_computations(
 
     def distribution(
         self,
-        parametrization_name: str | None = None,
+        parametrization_name: ParametrizationName | None = None,
+        sampling_strategy: SamplingStrategy | None = None,
+        computation_strategy: ComputationStrategy | None = None,
         **parameters_values: Any,
     ) -> ParametricFamilyDistribution:
         """
         Create a distribution instance with given parameters.
 
         Parameters
         ----------
-        parametrization_name : str, optional
+        parametrization_name : ParametrizationName | None, optional
             Name of parametrization to use (defaults to base).
+        sampling_strategy : SamplingStrategy
+            Strategy for generating random samples. Such an object is unique for each distribution.
+        computation_strategy : ComputationStrategy
+            Strategy for computing characteristics and conversions.
+            Such an object is unique for each distribution.
         **parameters_values
             Parameter values for the distribution.
 
@@ -324,22 +324,28 @@ def distribution(
         ValueError
             If parameters don't satisfy constraints.
         """
-        if parametrization_name is None:
-            parametrization_class = self.base
-        else:
-            parametrization_class = self._parametrizations[parametrization_name]
+        parametrization_class = (
+            self.base
+            if parametrization_name is None
+            else self._parametrizations[parametrization_name]
+        )
 
         parameters = parametrization_class(**parameters_values)
         parameters.validate()
         base_parameters = self.to_base(parameters)
         distribution_type = self._distr_type(base_parameters)
         return ParametricFamilyDistribution(
-            self.name, distribution_type, parameters, self.support_resolver(parameters)
+            family_name=self.name,
+            distribution_type=distribution_type,
+            parametrization=parameters,
+            support=self.support_resolver(parameters),
+            sampling_strategy=sampling_strategy,
+            computation_strategy=computation_strategy,
         )
 
     @dataclass_transform()
     def parametrization(
-        self, *, name: str
+        self, *, name: ParametrizationName
     ) -> Callable[[type[Parametrization]], type[Parametrization]]:
         """
         Create a class decorator that registers a parametrization.