quaquel
diff --git a/‎ema_workbench/em_framework/callbacks.py‎
Lines changed: 9 additions & 8 deletions b/‎ema_workbench/em_framework/callbacks.py‎
Lines changed: 9 additions & 8 deletions
diff --git a/‎ema_workbench/em_framework/evaluators.py‎
Lines changed: 3 additions & 3 deletions b/‎ema_workbench/em_framework/evaluators.py‎
Lines changed: 3 additions & 3 deletions
diff --git a/‎ema_workbench/em_framework/points.py‎
Lines changed: 20 additions & 38 deletions b/‎ema_workbench/em_framework/points.py‎
Lines changed: 20 additions & 38 deletions
@@ -18,8 +18,9 @@
 import numpy as np
 import pandas as pd
 
+from .outcomes import AbstractOutcome
 from ..util import ema_exceptions, get_module_logger
-from .parameters import BooleanParameter, CategoricalParameter, IntegerParameter
+from .parameters import BooleanParameter, CategoricalParameter, IntegerParameter, Parameter
 from .util import ProgressTrackingMixIn
 
 #
@@ -157,13 +158,13 @@ class DefaultCallback(AbstractCallback):
 
     def __init__(
         self,
-        uncertainties,
-        levers,
-        outcomes,
-        nr_experiments,
-        reporting_interval=100,
-        reporting_frequency=10,
-        log_progress=False,
+        uncertainties:list[Parameter],
+        levers:list[Parameter],
+        outcomes:list[AbstractOutcome],
+        nr_experiments:int,
+        reporting_interval:int=100,
+        reporting_frequency:int=10,
+        log_progress:bool=False,
     ):
         """Init.
 
 
@@ -337,7 +337,7 @@ def perform_experiments(
     lever_sampling_kwargs:dict|None=None,
     callback:type[AbstractCallback]|None=None,
     return_callback:bool=False,
-    combine:str="factorial",
+    combine:Literal["factorial", "sample"]="factorial",
     log_progress:bool=False,
     **kwargs,
 ) -> DefaultCallback:
@@ -382,7 +382,7 @@ def perform_experiments(
 
     """
     # TODO:: break up in to helper functions
-    # unreadable in this form
+    #        unreadable in this form
 
     if not scenarios and not policies:
         raise EMAError(
@@ -430,7 +430,7 @@ def perform_experiments(
                 f"{nr_of_exp} experiments"
             )
         case _:
-            raise ValueError(f'unknown value for combine, got {combine}, should be one of "zipover" or "factorial"')
+            raise ValueError(f'unknown value for combine, got {combine}, should be one of "sample" or "factorial"')
 
     callback = setup_callback(
         callback,
 
@@ -5,9 +5,9 @@
 """
 
 import itertools
-import random
 from collections import ChainMap
 from collections.abc import Iterable
+from typing import Literal
 
 from ema_workbench.em_framework.util import Counter, NamedDict, NamedObject, combine
 from ema_workbench.util import get_module_logger
@@ -19,7 +19,6 @@
     "Policy",
     "Scenario",
     "combine_cases_factorial",
-    "combine_cases_sampling",
     "experiment_generator",
 ]
 _logger = get_module_logger(__name__)
@@ -40,9 +39,10 @@ def __init__(self, name=None, unique_id=None, **kwargs):
         super().__init__(name, **kwargs)
         self.unique_id = unique_id
 
-    def __repr__(self): # noqa D105
+    def __repr__(self):  # noqa D105
         return f"Point({super().__repr__()})"
 
+
 class Policy(Point):
     """Helper class representing a policy.
 
@@ -66,7 +66,7 @@ def __init__(self, name=None, **kwargs):
     #
     #     return [self[param.name] for param in parameters]
 
-    def __repr__(self): # noqa D105
+    def __repr__(self):  # noqa D105
         return f"Policy({super().__repr__()})"
 
 
@@ -88,7 +88,7 @@ class Scenario(Point):
     def __init__(self, name=None, **kwargs):
         super().__init__(name, unique_id=Scenario.id_counter(), **kwargs)
 
-    def __repr__(self): # noqa: D105
+    def __repr__(self):  # noqa: D105
         return f"Scenario({super().__repr__()})"
 
 
@@ -105,14 +105,21 @@ class Experiment(NamedObject):
 
     """
 
-    def  __init__(self, name, model_name, policy, scenario, experiment_id):
+    def __init__(
+        self,
+        name: str,
+        model_name: str,
+        policy: Policy,
+        scenario: Scenario,
+        experiment_id: int,
+    ):
         super().__init__(name)
         self.experiment_id = experiment_id
         self.policy = policy
         self.model_name = model_name
         self.scenario = scenario
 
-    def __repr__(self): # noqa: D105
+    def __repr__(self):  # noqa: D105
         return (
             f"Experiment(name={self.name!r}, model_name={self.model_name!r}, "
             f"policy={self.policy!r}, scenario={self.scenario!r}, "
@@ -161,36 +168,6 @@ def zip_cycle(*args):
     return itertools.islice(zip(*(itertools.cycle(a) for a in args)), max_len)
 
 
-def combine_cases_sampling(*point_collection):
-    """Helper function for combining cases sampling.
-
-    Combine collections of cases by iterating over the longest collection
-    while sampling with replacement from the others.
-
-    Parameters
-    ----------
-    point_collection : collection of collection of Point instances
-
-    Yields
-    ------
-    Point
-
-    """
-
-    # figure out the longest
-    def exhaust_cases(cases):
-        return list(cases)
-
-    point_collection = [exhaust_cases(case) for case in point_collection]
-    longest_cases = max(point_collection, key=len)
-    other_cases = [case for case in point_collection if case is not longest_cases]
-
-    for case in longest_cases:
-        other = (random.choice(entry) for entry in other_cases)
-
-        yield Point(**ChainMap(case, *other))
-
-
 def combine_cases_factorial(*point_collections):
     """Combine collections of cases in a full factorial manner.
 
@@ -234,7 +211,12 @@ def combine_cases_factorial(*point_collections):
 #     return combined_cases
 
 
-def experiment_generator(models:Iterable["AbstractModel"], scenarios:Iterable[Scenario], policies:Iterable[Policy], combine:str="factorial"):
+def experiment_generator(
+    models: Iterable["AbstractModel"],
+    scenarios: Iterable[Scenario],
+    policies: Iterable[Policy],
+    combine: Literal["factorial", "sample"] = "factorial",
+):
     """Generator function which yields experiments.
 
     Parameters