docs: remove distinction between poolers and samplers in experimentalist.md

docs/contribute/modules/experimentalist.md

@@ -7,11 +7,6 @@ by an experiment runner. Experimentalists are generally implemented as functions
 
 ![Experimentalist Module](../../img/experimentalist.png)
 
-Experimentalists can be implemented as *poolers* or as *samplers*.
-- **Poolers** return a pool of candidate experimental conditions, which can be passed to a sampler that selects
-  a subset of conditions from the pool to be used in the next experiment.
-- **Samplers** directly return a subset of experimental conditions from a pool of candidate experimental conditions that already exist.
-
 ## Repository Setup
 
 We recommend using the [cookiecutter template](https://github.com/AutoResearch/autora-template-cookiecutter) to set up
@@ -22,80 +17,30 @@ a repository for your experimentalist. Alternatively, you can use the
 cookiecutter https://github.com/AutoResearch/autora-template-cookiecutter
 ```
 
-Make sure to select the `experimentalist` option when prompted. You may also select whether you want to implement an experimentalist as a sampler, pooler, or custom function. You can skip all other prompts pertaining to other modules 
+Make sure to select the `experimentalist` option when prompted. You can skip all other prompts pertaining to other modules 
 (e.g., experiment runners) by pressing enter.
 
 ## Implementation
 
-Irrespective of whether you are implementing a pooler or a sampler, 
-you should implement a function that returns a set of experimental conditions. This set may be
+For an experimentalist, you should implement a function that returns a set of experimental conditions. This set may be
 a numpy array, iterator variable or other data format. 
 
 !!! hint
     We generally **recommend using 2-dimensional numpy arrays as outputs** in which
     each row represents a set of experimental conditions. The columns of the array correspond to the independent variables.
 
-### Implementing Poolers
-
-Once you've created your repository, you can implement your experimentalist pooler by editing the `init.py` file in 
-``src/autora/experimentalist/pooler/name_of_your_experimentalist/``. 
+Once you've created your repository, you can implement your experimentalist by editing the `init.py` file in 
+``src/autora/experimentalist/name_of_your_experimentalist/``. 
 You may also add additional files to this directory if needed. 
 It is important that the `init.py` file contains a function called `name_of_your_experimentalist` 
-which returns a pool of experimental conditions (e.g., as an iterator object or numpy array).
-
-The following example ``init.py`` illustrates the implementation of a simple experimentalist pooler
-that generates a grid of samples within the specified bounds of each independent variable (IV):
-
-```python 
-
-"""
-Example Experimentalist Pooler
-"""
-
-from itertools import product
-from typing import List
-from autora.variable import IV
-
-
-def grid_pool(ivs: List[IV]):
-    """
-    Creates exhaustive pool from discrete values using a Cartesian product of sets
-
-    Arguments:
-        ivs {List[IV]}:  List of independent variables
+which returns a set of experimental conditions (e.g., as a numpy array).
 
-    Returns:
-        pool: An iterator over all possible combinations of IV values
-    """
-
-    l_iv_values = []
-    for iv in ivs:
-        assert iv.allowed_values is not None, (
-            f"gridsearch_pool only supports independent variables with discrete allowed values, "
-            f"but allowed_values is None on {iv=} "
-        )
-        l_iv_values.append(iv.allowed_values)
-
-    # Return Cartesian product of all IV values
-    return product(*l_iv_values)
-
-
-```
-
-### Implementing Samplers
-
-Once you've created your repository, you can implement your experimentalist sampler by editing the `init.py` file in 
-``src/autora/experimentalist/sampler/name_of_your_experimentalist/``. 
-You may also add additional files to this directory if needed. 
-It is important that the `init.py` file contains a function called `name_of_your_experimentalist` 
-which returns a set of experimental conditions (e.g., as a numpy array) given a pool of candidate experimental conditions.
-
-The following example ``init.py`` illustrates the implementation of a simple experimentalist sampler
+The following example ``init.py`` illustrates the implementation of a simple experimentalist
 that uniformly samples without replacement from a pool of candidate conditions.
 
 ```python 
 """
-Example Experimentalist Sampler
+Example Experimentalist
 """
 
 import random
@@ -120,7 +65,6 @@ random_sample(conditions: Union[Iterable, Sequence], n: int = 1):
     return samples
 ```
 
-
 ## Next Steps: Testing, Documentation, Publishing
 
 For more information on how to test, document, and publish your experimentalist, please refer to the