additional rearranging and clarifications

Author: jlnav

AGENTS.md

@@ -5,6 +5,7 @@ Read the ``README.rst`` for an overview of libEnsemble.
 
 - libEnsemble uses a manager-worker architecture. Points are generated by a generator and sent to a worker, which runs a simulator.
 - The manager determines how and when points get passed to workers via an allocation function.
+- See ``libensemble/tests/regression_tests/test_1d_sampling.py`` for a simple example of the libEnsemble interface.
 
 Repository Layout
 -----------------
@@ -28,7 +29,7 @@ Repository Layout
 -   ``generators.py`` - Base classes for generators that adhere to the `gest-api` standard.
 -   ``history.py`` - Module for recording points that have been generated and simulation results. NumPy array.
 -   ``libE.py`` - libE main file. Previous primary interface for parameterizing and running libEnsemble.
--   ``logger.py`` - Logging configuration
+-   ``logger.py`` - Logging configuration.
 -   ``manager.py`` - Module for maintaining the history array and passing points between the workers.
 -   ``message_numbers.py`` - Constants that represent states of the ensemble.
 -   ``specs.py`` - Dataclasses for parameterizing the ensemble. Most importantly, contains ``LibeSpecs, SimSpecs, GenSpecs``.
@@ -44,53 +45,51 @@ Repository Layout
 
 Other files in the root directory should be self-documenting.
 
-Familiarize yourself with ``libensemble/tests/regression_tests/test_1d_sampling.py`` for a simple example of the libEnsemble interface.
-
 Information about Generators
 ----------------------------
 
 - Generators are functions or objects that produce points for simulations.
-- The History array is a numpy structured array that stores points that have been generated and simulation results. Its fields match ``sim_specs/gen_specs["out"]`` or ``vocs`` attributes.
-- Prior to libEnsemble v1.6.0, generators were plain functions. They often ran in "persistent" mode, meaning they executed in a long-running loop, sending and receiving points to and from the manager until the ensemble was complete.
+- The History array is a NumPy structured array that stores points that have been generated and simulation results.
+Its fields match ``sim_specs/gen_specs["out"]`` or ``vocs`` attributes, plus additional reserved fields for metadata.
+- Prior to libEnsemble v1.6.0, generators were plain functions. They often ran in "persistent" mode, meaning they executed in a
+long-running loop, sending and receiving points to and from the manager until the ensemble was complete.
 - A ``gest-api`` or "standardized" generator is a class that at a minimum implements ``suggest`` and ``ingest`` methods, and is parameterized by a ``vocs``.
 - See ``libensemble/generators.py`` for more information about the ``gest-api`` standard.
+- If using a generator that adheres to the ``gest-api`` standard, or a classic persistent generator, use the ``start_only_persistent`` allocation function.
+- Generators are often used for simple sampling, optimization, calibration, uncertainty quantification, and other simulation-based tasks.
 
 General Guidelines
 ------------------
 
 - If using classic ``sim_specs`` and ``gen_specs``, then ensure that ``sim_specs["out"]`` and ``gen_specs["in"]`` field names match, and vice-versa.
 - As-of libEnsemble v1.6.0, ``SimSpecs`` and ``GenSpecs`` can also be parameterized by a ``vocs`` object, imported from ``gest_api.vocs`` (NOT xopt.vocs).
-- ``VOCS`` contains variables, objectives, constraints, and other settings that define the problem. See ``libensemble/tests/regression_tests/test_xopt_EI.py`` for an example of how to use it.
-- If using a generator that adheres to the ``gest-api`` standard, or a classic persistent generator, use the ``start_only_persistent`` allocation function.
+- ``VOCS`` contains variables, objectives, constraints, and other settings that define the problem.
+See ``libensemble/tests/regression_tests/test_xopt_EI.py`` for an example of how to use it.
 - An MPI distribution is not required for libEnsemble to run, but is required to use the ``MPIExecutor``.  ``mpich`` is recommended.
 - New tests are heavily encouraged for new features, bug fixes, or integrations. See ``libensemble/tests/regression_tests`` for examples.
 - Never use destructive git commands unless explicitly requested.
 - Code is in the ``black`` style. This should be enforced by ``pre-commit``.
 - When writing new code, prefer the ``LibeSpecs``, ``SimSpecs``, and ``GenSpecs`` dataclasses over the classic ``sim_specs`` and ``gen_specs`` bare dictionaries.
 - Read ``CONTRIBUTING.md`` for more information.
+- The external ``libE-community-examples`` repository contains past use-cases, generators, and other examples.
 
 Development Environment
 -----------------------
 
 - ``pixi`` is the recommended environment manager for libEnsemble development.  See ``pyproject.toml`` for the list
 of dependencies and the available testing environments.
-
 - Enter the development environment with ``pixi shell -e dev``. This environment contains the most common dependencies for development and testing.
 - For one-off commands, use ``pixi run -e dev``. This will run a single command in the development environment.
-
-- If ``pixi`` is not available or not preferred by the user, ``pip`` can be used instead, but the user will need to manually install other dependencies.
-
+- If ``pixi`` is not available or not preferred by the user, ``pip install -e .`` can be used instead. Other dependencies may need to be installed manually.
 - If committing, use ``pre-commit`` to ensure that code style and formatting are consistent.  See ``.pre-commit-config.yaml`` for
 the configuration and ``pyproject.toml`` for other configuration.
 
 Testing
 -------
 
 - Run tests with the ``run-tests.py`` script: ``python libensemble/tests/run-tests.py``.  See ``libensemble/tests/run-tests.py`` for usage information.
-
-- Some tests require third party software to be installed. When developing a feature or fixing a bug the entire test suite may not necessarily need to be run,
-since it will be on Github Actions.
-
+- Some tests require third party software to be installed. When developing a feature or fixing a bug, since the entire test suite will be run on Github Actions,
+for local development running individual tests is sufficient.
 - Individual unit tests can be run with ``pixi run -e dev pytest path/to/test_file``.
 - A libEnsemble run typically outputs an ``ensemble.log`` and ``libE_stats.txt`` file in the working directory. Check these files for tracebacks or run statistics.
 - An "ensemble" or "workflow" directory may also be created, often containing per-simulation output directories