Fixes and updates for differential parmater sweep tool

docs/how_to_guides/how_to_run_differential_parameter_sweep.rst

@@ -185,8 +185,8 @@ With the flowsheet defined and suitably initialized, along with the definitions
 
     # Run the parameter sweep
     global_results = differential_parameter_sweep(
-            model, 
-            sweep_params, 
+            build_model, 
+            build_sweep_params, 
             differential_sweep_specs,
             outputs, 
             h5_results_file_name='monte_carlo_results.h5',

docs/how_to_guides/how_to_use_loopTool_to_explore_flowsheets.rst

@@ -80,7 +80,7 @@ This enables user to have multiple setup files that can be used to run simulatio
    * *build_defaults* – default arguments that are passed for every sweep. For example in ro_erd flowsheet, shown here, this could be erd_type, but in other flowsheets, this could define any default options, from file locations to the number of stages, etc. The defaults will be passed along with loop values unless the loop value overrides them. 
    * *init_defaults* - defaults for initialization_function, same behavior as build-defaults but used for initialization_function
    * *optimize_defaults* - defaults for optimize_function, same behavior as build or init defaults, but for optimize function. 
-   * *build_output_kwargs * - a list of keys to only save in .h5 file. loopTool provides a default build_outputs function that takes in a dict containing model key name and model key (e.g., to output only m.fs.costing.LCOW user would include following dict *LCOW : fs.costing.LCOW*). The default function uses model.find_component to construct an output dict. Alternatively user can provide  kwargs to a user-provided build_outputs function that would be linked to loopTool (shown below). 
+   * *build_output_kwargs* - a list of keys to only save in .h5 file. loopTool provides a default build_outputs function that takes in a dict containing model key name and model key (e.g., to output only m.fs.costing.LCOW user would include following dict *LCOW : fs.costing.LCOW*). The default function uses model.find_component to construct an output dict. Alternatively user can provide  kwargs to a user-provided build_outputs function that would be linked to loopTool (shown below). 
 
 **loop options:**
 

tutorials/assets_parameter_sweep_demo/parameter_sweep_demo_script.py

@@ -51,7 +51,6 @@ def build_sweep_params(m, num_samples=1, scenario="A_comp_vs_LCOW"):
 
 
 def run_parameter_sweep(num_samples=100, num_procs=1):
-
     ps1, kwargs_dict1 = create_parameter_sweep_object(
         num_samples, num_procs, parallel_backend="ConcurrentFutures"
     )
@@ -103,7 +102,6 @@ def create_parameter_sweep_object(
     num_procs,
     parallel_backend="ConcurrentFutures",
 ):
-
     solver = get_solver()
     kwargs_dict = {
         "debugging_data_dir": None,
@@ -149,7 +147,6 @@ def create_recursive_parameter_sweep_object(
     num_procs,
     parallel_backend="ConcurrentFutures",
 ):
-
     solver = get_solver()
     kwargs_dict = {
         "debugging_data_dir": None,
@@ -195,19 +192,20 @@ def create_differential_parameter_sweep_object(
     num_procs,
     parallel_backend="ConcurrentFutures",
 ):
-
     solver = get_solver()
     m = build_model(read_model_defauls_from_file=False)
 
-    differential_sweep_specs = {
-        "A_comp": {
-            "diff_mode": "sum",
-            "diff_sample_type": UniformSample,
-            "relative_lb": 0.01,
-            "relative_ub": 0.01,
-            "pyomo_object": m.fs.RO.A_comp,
+    def build_spec(model):
+        differential_sweep_specs = {
+            "A_comp": {
+                "diff_mode": "sum",
+                "diff_sample_type": UniformSample,
+                "relative_lb": 0.01,
+                "relative_ub": 0.01,
+                "pyomo_object": model.fs.RO.A_comp,
+            }
         }
-    }
+        return differential_sweep_specs
 
     kwargs_dict = {
         "debugging_data_dir": None,
@@ -228,8 +226,8 @@ def create_differential_parameter_sweep_object(
         "build_outputs_kwargs": {},
         "optimize_function": optimize,
         "optimize_kwargs": {"solver": solver, "check_termination": False},
-        "num_diff_samples": 2,
-        "differential_sweep_specs": differential_sweep_specs,
+        "num_diff_samples": 1,
+        "build_differential_sweep_specs": build_spec,
         "initialize_function": None,
         "update_sweep_params_before_init": False,
         "initialize_kwargs": {},
@@ -252,7 +250,6 @@ def create_differential_parameter_sweep_object(
 
 
 if __name__ == "__main__":
-
     import sys
     import time
     import numpy as np

tutorials/parameter_sweep_demo.ipynb

@@ -192,62 +192,6 @@
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a9de9f80",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "??build_model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d106ae65",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "??build_outputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b07c1657",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "??optimize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "015e21aa",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "??build_sweep_params"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -442,20 +386,6 @@
     "*Run the simple parameter sweep in recursion if some runs fail to ensure that a user-specified number of sample results are generated. This involves resampling the input parameter space to compensate for the failed runs.*"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0d963217",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "??create_recursive_parameter_sweep_object"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -537,16 +467,6 @@
     "* The differential sweep is a simple parameter sweep that occurs at every nominal value, where one of the sweep parameters is perturbed keeping the others fixed to their nominal values."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5030e928",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "??create_differential_parameter_sweep_object"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -557,7 +477,8 @@
    "outputs": [],
    "source": [
     "num_samples = 2\n",
-    "num_procs = 2\n",
+    "num_procs = 1\n",
+    "\n",
     "model, dps, dkwargs_dict = create_differential_parameter_sweep_object(num_samples, num_procs)\n",
     "dps_results_array, dps_results_dict = dps.parameter_sweep(\n",
     "    dkwargs_dict[\"build_model\"],\n",
@@ -874,7 +795,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.9.16"
   },
   "rise": {
    "enable_chalkboard": true,

watertap/tools/analysis_tools/loop_tool/loop_tool.py

@@ -723,8 +723,12 @@ def run_diff_parameter_sweep(self):
             ps_kwargs["build_outputs_kwargs"] = self.build_outputs_kwargs
 
         ps_kwargs[
-            "differential_sweep_specs"
-        ] = ParameterSweepReader()._dict_to_diff_spec(m, self.differential_sweep_specs)
+            "build_differential_sweep_specs"
+        ] = ParameterSweepReader()._dict_to_diff_spec
+        ps_kwargs["build_differential_sweep_specs_kwargs"] = {
+            "input_dict": self.differential_sweep_specs
+        }
+        ps_kwargs["num_diff_samples"] = self.diff_samples
         ps = DifferentialParameterSweep(**ps_kwargs)
 
         ps.parameter_sweep(

watertap/tools/parameter_sweep/model_manager.py

@@ -27,6 +27,9 @@ def __init__(self, ps_instance):
         self.is_initialized = False
         self.is_solved = False
         self.is_prior_parameter_solved = False
+        # internal state option to be used by differntial sweep tool to
+        # prevent model reintialization and state reset
+        self._is_rebuild_and_init_enabled = True
         # this is isused for loggin states if enabled
         self.solved_states = {"state": [], "local_value_k": []}
         self.initialized_states = {"state": [], "local_value_k": []}