Parameter Sweep migration

.github/workflows/checks.yml

@@ -111,9 +111,6 @@ jobs:
         echo '::group::Output of "pip install" commands'
         pip install -r requirements-dev.txt
         echo '::endgroup::'
-        echo '::group::Output of "pip install -U ray" command'
-        pip install -U ray
-        echo '::endgroup::'
         echo '::group::Output of "conda install -c conda-forge cyipopt" command'
         conda install -c conda-forge cyipopt "numpy<2"
         echo '::endgroup::'

docs/conf.py

@@ -99,6 +99,7 @@
 # intersphinx mapping to idaes
 intersphinx_mapping = {
     "idaes": ("https://idaes-pse.readthedocs.io/en/stable/", None),
+    "parameter_sweep": ("https://parameter-sweep.readthedocs.io/en/latest/", None),
 }
 
 rst_epilog = """

docs/how_to_guides/how_to_run_differential_parameter_sweep.rst

@@ -29,7 +29,7 @@ Once this is done, we import the differential parameter sweep tool and sampling
 
 .. testcode::
 
-    from watertap.tools.parameter_sweep import differential_parameter_sweep, UniformSample, NormalSample
+    from parameter_sweep import differential_parameter_sweep, UniformSample, NormalSample
 
 We will use the :ref:`same setup steps as before<how_to_use_parameter_sweep>` to set up the generating functions for our model, sweep params, and outputs:
 
@@ -214,4 +214,5 @@ With the flowsheet defined and suitably initialized, along with the definitions
 Module Documentation
 --------------------
 
-* :mod:`watertap.tools.parameter_sweep`
+* :mod:`parameter_sweep`
+* :mod:`parameter_sweep.differential`

docs/how_to_guides/how_to_use_parameter_sweep.rst

@@ -41,7 +41,7 @@ Once this is done, import the parameter sweep tool
 
 .. testcode::
 
-    from watertap.tools.parameter_sweep import parameter_sweep, LinearSample
+    from parameter_sweep import parameter_sweep, LinearSample
 
 Conceptually, regardless of the number of iterations necessary to test each possible combination of variables, it is only necessary to build, simulate, and set up the model once.
 Thus, these steps are left to the user and handled outside the parameter sweep function.
@@ -158,4 +158,4 @@ Note that there are additional keyword arguments that can be passed to this func
 Module Documentation
 --------------------
 
-* :mod:`watertap.tools.parameter_sweep`
+* :mod:`parameter_sweep`

docs/how_to_guides/how_to_use_parameter_sweep_monte_carlo.rst

@@ -34,7 +34,7 @@ Once this is done, we import the parameter sweep tool and two different random s
 
 .. testcode::
 
-    from watertap.tools.parameter_sweep import parameter_sweep, UniformSample, NormalSample
+    from parameter_sweep import parameter_sweep, UniformSample, NormalSample
 
 The parameter sweep tool currently offers 6 classes that can be broadly categorized under the following 3 categroies:
 
@@ -132,4 +132,4 @@ which will parallelize the requested parameter sweep between 4 computational uni
 Module Documentation
 --------------------
 
-* :mod:`watertap.tools.parameter_sweep`
+* :mod:`parameter_sweep`

docs/technical_reference/tools/index.rst

@@ -1,8 +1,5 @@
 Tools for Flowsheet Analysis
 ============================
 
-.. toctree::
-   :maxdepth: 2
-
-   parameter_sweep
-   parallel_manager
+* `Parameter Sweep <https://parameter-sweep.readthedocs.io/en/latest/getting_started/parameter_sweep.html>`_
+* `Parallel Manager <https://parameter-sweep.readthedocs.io/en/latest/getting_started.html>`_

setup.py

@@ -83,9 +83,7 @@
         "pyomo>=6.6.1",
         "pyyaml",  # watertap.core.wt_database
         # for parameter_sweep
-        "h5py",
-        "requests",
-        "scipy",
+        "parameter-sweep>=0.1.dev5",
         # for watertap.ui.api_model (though may be generally useful)
         "pydantic >= 2, <3",
         "numpy",

tutorials/assets_parameter_sweep_demo/parameter_sweep_demo_script.py

@@ -17,7 +17,7 @@
     build_model,
     build_outputs,
 )
-from watertap.tools.parameter_sweep import (
+from parameter_sweep import (
     LinearSample,
     UniformSample,
     ParameterSweep,