Merge branch 'develop' of https://github.com/Libensemble/libensemble into develop

Author: jlnav

.github/workflows/basic.yml

@@ -62,6 +62,12 @@ jobs:
             pip install -e .
             flake8 libensemble
 
+        - name: Install mypy
+          run: pip install mypy
+
+        - name: Run mypy (limited scope)
+          run: mypy
+
         - name: Remove various tests on newer pythons
           if: matrix.python-version == 'py311' || matrix.python-version == 'py312' || matrix.python-version == 'py313' || matrix.python-version == 'py314'
           run: |

docs/overview_usecases.rst

@@ -8,8 +8,8 @@ Manager, Workers, Generators, and Simulators
 libEnsemble's **manager** allocates work to **workers**,
 which perform computations via **generators** and **simulators**:
 
-* :ref:`generator<api_gen_f>`: Generates inputs to the *simulator*
-* :ref:`simulator<api_sim_f>`: Performs an evaluation based on parameters from the *generator*
+* :ref:`generator<api_gen_f>`: Generates inputs for the *simulator*
+* :ref:`simulator<api_sim_f>`: Performs an evaluation using parameters from the *generator*
 
 .. figure:: images/adaptiveloop.png
   :alt: Adaptive loops
@@ -28,19 +28,19 @@ which perform computations via **generators** and **simulators**:
 An :doc:`executor<executor/overview>` interface is available so generators and simulators
 can launch and monitor external applications.
 
-libEnsemble uses a NumPy structured array known as the :ref:`history array<funcguides-history>`
-to keep a record of all simulations and generated values.
+libEnsemble uses a NumPy structured array called the :ref:`history array<funcguides-history>`
+to record all simulations and generated values.
 
 Allocator Function
 ~~~~~~~~~~~~~~~~~~
 
 * :ref:`allocator<api_alloc_f>`: Decides whether a simulator or generator should be
-  prompted (and with what inputs/resources) as workers become available
+  invoked (and with what inputs/resources) as workers become available
 
-The default allocator (``alloc_f``) prompts workers to run the highest priority simulator work.
-If a worker is idle and there is no simulator work, that worker is prompted to query the generator.
+The default allocator (``alloc_f``) prompts workers to run the highest-priority simulator work.
+If a worker is idle and no simulator work is available, that worker is prompted to query the generator.
 
-The default allocator is appropriate for the vast majority of use-cases, but is customizable
+The default allocator is appropriate for the majority of use cases but can be customized
 for users interested in more advanced allocation strategies.
 
 Example Use Cases
@@ -53,104 +53,102 @@ to support):
 .. dropdown:: **Click Here for Use-Cases**
 
   * A user wants to optimize a simulation calculation. The simulation may
-    already be using parallel resources but not a large fraction of some
-    computer. libEnsemble can coordinate the concurrent evaluation of the
-    simulation ``sim_f`` at various parameter values based on candidate parameter
-    values from ``gen_f`` (possibly after each ``sim_f`` output).
+    already be using parallel resources but not a large fraction of a 
+    computer. libEnsemble can coordinate concurrent evaluations of the 
+    simulation ``sim_f`` at multiple parameter values based on candidate parameter
+    values produced by ``gen_f`` (possibly after each ``sim_f`` output).
 
   * A user has a ``gen_f`` that produces meshes for a
-    ``sim_f``. Given the ``sim_f`` output, the ``gen_f`` can refine a mesh or
-    produce a new mesh. libEnsemble can ensure that the calculated meshes can be
-    used by multiple simulations without requiring moving data.
+    ``sim_f``. Based on the ``sim_f`` output, the ``gen_f`` can refine a mesh or
+    produce a new mesh. libEnsemble ensures that generated meshes can be
+    reused by multiple simulations without requiring data movement.
 
   * A user wants to evaluate a simulation ``sim_f`` with different sets of
     parameters, each drawn from a set of possible values. Some parameter values
     are known to cause the simulation to fail. libEnsemble can stop
     unresponsive evaluations and recover computational resources for future
-    evaluations. The ``gen_f`` can possibly update the sampling after discovering
+    evaluations. The ``gen_f`` can update the sampling strategy after discovering
     regions where evaluations of ``sim_f`` fail.
 
   * A user has a simulation ``sim_f`` that requires calculating multiple
     expensive quantities, some of which depend on other quantities. The ``sim_f``
-    can observe intermediate quantities to stop related calculations and
+    can monitor intermediate quantities to stop related calculations early and
     preempt future calculations associated with poor parameter values.
 
-  * A user has a ``sim_f`` with multiple fidelities, with the higher-fidelity
-    evaluations requiring more computational resources, and a
-    ``gen_f``/``alloc_f`` that decides which parameters should be evaluated and
-    at what fidelity level. libEnsemble can coordinate these evaluations without
-    requiring the user to know parallel programming.
+  * A user has a ``sim_f`` with multiple fidelities, where higher-fidelity
+    evaluations require more computational resources. A ``gen_f``/``alloc_f``
+    pair decides which parameters should be evaluated and
+    at what fidelity level. libEnsemble coordinates these evaluations without
+    requiring the user to write parallel code.
 
-  * A user wishes to identify multiple local optima for a ``sim_f``. Furthermore,
+  * A user wishes to identify multiple local optima for a ``sim_f``. In addition,
     sensitivity analysis is desired at each identified optimum. libEnsemble can
-    use the points from the APOSMM ``gen_f`` to identify optima; and after a
-    point is ruled to be an optimum, a different ``gen_f`` can produce a
-    collection of parameters necessary for sensitivity analysis of ``sim_f``.
+    use points from the APOSMM ``gen_f`` to identify optima. After a point is
+    determined to be an optimum, a different ``gen_f`` can generate the
+    parameter sets required for sensitivity analysis of ``sim_f``.
 
-  Combinations of these use cases are supported as well. An example of
-  such a combination is using libEnsemble to solve an optimization problem that
-  relies on simulations that fail frequently.
+  Combinations of these use cases are also supported. For example, libEnsemble
+  can be used to solve optimization problems where simulations fail
+  frequently.
 
 Glossary
 ~~~~~~~~
 
 Here we define some terms used throughout libEnsemble's code and documentation.
-Although many of these terms seem straightforward, defining such terms assists
-with keeping confusion to a minimum when communicating about libEnsemble and
+Although many of these terms seem straightforward, defining them helps reduce
+confusion when communicating about libEnsemble and
 its capabilities.
 
 .. dropdown:: **Click Here for Glossary**
   :open:
 
-  * **Manager**: Single libEnsemble process facilitating communication between
-    other processes. Within libEnsemble, the *Manager* process configures and
-    passes work to and from the workers.
+  * **Manager**: A single libEnsemble process that facilitates communication between
+    other processes. The *Manager* configures and distributes work to
+    workers and collects their output.
 
   * **Worker**: libEnsemble processes responsible for performing units of work,
-    which may include submitting or executing tasks. *Worker* processes run
-    generation and simulation routines, submit additional tasks for execution,
-    and return results to the manager.
+    which may include executing tasks or submitting external jobs. Workers run
+    generation and simulation routines and return results to the manager.
 
   * **Calling Script**: libEnsemble is typically imported, parameterized, and
     initiated in a single Python file referred to as a *calling script*. ``sim_f``
-    and ``gen_f`` functions are also commonly configured and parameterized here.
+    and ``gen_f`` functions are commonly configured and parameterized here.
 
   * **User function**: A generator, simulator, or allocation function. These
-    are Python functions that govern the libEnsemble workflow. They
+    Python functions govern the libEnsemble workflow. They
     must conform to the libEnsemble API for each respective user function, but otherwise can
-    be created or modified by the user. libEnsemble comes with many examples of
-    each type of user function.
+    be created or modified by the user. 
+    libEnsemble includes many examples of each type.
 
-  * **Executor**: The executor can be used within user functions to provide a
-    simple, portable interface for running and managing user tasks (applications).
-    There are multiple executors including the base ``Executor`` and ``MPIExecutor``.
+  * **Executor**: The executor provides a simple, portable interface for
+    launching and managing user tasks (applications). Multiple executors are
+    available, including the base ``Executor`` and ``MPIExecutor``.
 
-  * **Submit**: Enqueue or indicate that one or more jobs or tasks need to be
-    launched. When using the libEnsemble Executor, a *submitted* task is executed
+  * **Submit**: To enqueue or indicate that one or more jobs or tasks should be
+    launched. When using the libEnsemble Executor, a *submitted* task is either executed
     immediately or queued for execution.
 
-  * **Tasks**: Sub-processes or independent units of work. Workers perform
-    *tasks* as directed by the manager; tasks may include submitting external
+  * **Tasks**: Subprocesses or independent units of work. Workers perform
+    tasks as directed by the manager. Tasks may include launching external
     programs for execution using the Executor.
 
   * **Persistent**: Typically, a worker communicates with the manager
-    before and after initiating a user ``gen_f`` or ``sim_f`` calculation. However, user
-    functions may also be constructed to communicate directly with the manager,
-    for example, to efficiently maintain and update data structures instead of
-    communicating them between manager and worker. These calculations
-    and the workers assigned to them are referred to as *persistent*.
-
-  * **Resource Manager** libEnsemble has a built-in resource manager that can detect
-    (or be provided with) a set of resources (e.g., a node-list). Resources are
-    divided up among workers (using *resource sets*) and can be dynamically
+    before and after initiating a user ``gen_f`` or ``sim_f`` calculation. Persistent user
+    functions instead communicate directly with the manager during execution,
+    allowing them to maintain and update data structures efficiently. These
+    calculations and their assigned workers are referred to as *persistent*.
+
+  * **Resource Manager**: libEnsemble includes a built-in resource manager that can detect 
+    (or be provided with) available resources (e.g., a node list). Resources are
+    divided among workers using *resource sets* and can be dynamically
     reassigned.
 
   * **Resource Set**: The smallest unit of resources that can be assigned (and
-    dynamically reassigned) to workers. By default it is the provisioned resources
-    divided by the number of workers (excluding any workers given in the
-    ``zero_resource_workers`` libE_specs option). However, it can also be set
-    directly by the ``num_resource_sets`` libE_specs option.
+    dynamically reassigned) to workers. By default this is the provisioned resources
+    divided by the number of workers (excluding any workers listed in the 
+    ``zero_resource_workers`` ``libE_specs`` option). It can also be set
+    explicitly using the ``num_resource_sets`` ``libE_specs`` option.
 
-  * **Slot**: The ``resource sets`` enumerated on a node (starting with zero). If
-    a resource set has more than one node, then each node is considered to have slot
+  * **Slot**: Resource sets enumerated on a node (starting from zero). If
+    a resource set spans multiple nodes, each node is considered to have slot
     zero.

docs/spelling_wordlist.txt

@@ -66,7 +66,7 @@ init
 instantiation
 intercommunications
 interoperable
-intitializing
+initializing
 intranode
 ints
 invocable

examples/tutorials/aposmm/aposmm_tutorial_notebook.ipynb

@@ -369,8 +369,7 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.1"
+   "pygments_lexer": "ipython3"
   }
  },
  "nbformat": 4,

examples/tutorials/xopt_bayesian_gen/xopt_EI_example.ipynb

@@ -256,8 +256,7 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.11"
+   "pygments_lexer": "ipython3"
   }
  },
  "nbformat": 4,

install/misc_feature_requirements.txt

@@ -1 +1 @@
-globus-compute-sdk==4.6.0
+globus-compute-sdk==4.7.0

libensemble/tests/functionality_tests/test_asktell_sampling_external_gen.py

@@ -18,15 +18,16 @@
 import numpy as np
 from gest_api.vocs import VOCS
 
-# from gest_api.vocs import ContinuousVariable
+from libensemble import Ensemble
 
 # Import libEnsemble items for this test
 from libensemble.alloc_funcs.start_only_persistent import only_persistent_gens as alloc_f
 
 # from libensemble.gen_classes.external.sampling import UniformSampleArray
 from libensemble.gen_classes.external.sampling import UniformSample
-from libensemble import Ensemble
-from libensemble.specs import GenSpecs, SimSpecs, AllocSpecs, ExitCriteria, LibeSpecs
+from libensemble.specs import AllocSpecs, ExitCriteria, GenSpecs, LibeSpecs, SimSpecs
+
+# from gest_api.vocs import ContinuousVariable
 
 
 def sim_f_array(In):

libensemble/tests/regression_tests/support.py

@@ -1,6 +1,7 @@
 import copy
 
 import numpy as np
+
 from libensemble.specs import input_fields, output_data
 
 branin_vals_and_minima = np.array(

libensemble/tests/regression_tests/test_1d_sampling.py

@@ -14,6 +14,7 @@
 # TESTSUITE_NPROCS: 2 4
 
 import numpy as np
+
 from libensemble import Ensemble
 from libensemble.gen_funcs.sampling import latin_hypercube_sample as gen_f
 

libensemble/tests/regression_tests/test_2d_sampling.py

@@ -14,6 +14,7 @@
 # TESTSUITE_NPROCS: 2 4
 
 import numpy as np
+
 from libensemble import Ensemble
 from libensemble.gen_funcs.sampling import latin_hypercube_sample as gen_f