Merge branch 'main' into update_dl_doc

docs/explanations/modeling_extensions/diagnostics/index.rst

@@ -5,3 +5,6 @@ Degeneracy Hunter
 Degeneracy Hunter is a collection of tools for diagnostics of mathematical programs. The core ideas behind Degeneracy Hunter are explained here: https://www.sciencedirect.com/science/article/pii/B9780444635785501304
 
 Degeneracy Hunter is currently included in IDAES for beta testing purposes. This page will be updated as part of the official release. If you would like to help test Degeneracy Hunter, please look at the example notebook available here: https://github.com/IDAES/examples-pse/pull/21 Please report any thoughts, questions or bugs to: adowling@nd.edu
+
+Full documentation of the available methods can be :ref:`found here<reference_guides/core/util/model_diagnostics:Degeneracy Hunter>`.
+

docs/reference_guides/core/util/index.rst

@@ -7,6 +7,7 @@
     dyn_utils
     initialization
     misc
+    model_diagnostics
     model_serializer
     model_statistics
     phase_equilibria

docs/reference_guides/core/util/model_diagnostics.rst

@@ -0,0 +1,22 @@
+Model Diagnostic Functions
+==========================
+
+The IDAES toolset contains a number of utility functions which can be useful for identifying modeling issues and debugging solver issues.
+
+.. contents:: Contents
+    :depth: 2
+
+Degeneracy Hunter
+^^^^^^^^^^^^^^^^^
+
+.. autoclass:: idaes.core.util.model_diagnostics.DegeneracyHunter
+    :members:
+
+
+Available Methods
+^^^^^^^^^^^^^^^^^
+
+.. automodule:: idaes.core.util.model_diagnostics
+    :exclude-members: DegeneracyHunter
+    :members:
+

idaes/apps/uncertainty_propagation/tests/test_uncertainties.py

@@ -10,28 +10,31 @@
 # Please see the files COPYRIGHT.md and LICENSE.md for full copyright and
 # license information.
 #################################################################################
-import sys
 import os
-from unittest.mock import patch
-
-sys.path.append(os.path.abspath(".."))  # current folder is ~/tests
 import numpy as np
 import pandas as pd
-from scipy import sparse
 import pytest
-from pytest import approx
+
+from pyomo.environ import (
+    ConcreteModel,
+    Constraint,
+    exp,
+    Objective,
+    SolverFactory,
+    Suffix,
+    Var,
+)
+import pyomo.contrib.parmest.parmest as parmest
+
 from idaes.apps.uncertainty_propagation.uncertainties import (
     quantify_propagate_uncertainty,
     propagate_uncertainty,
     clean_variable_name,
 )
-from pyomo.opt import SolverFactory
-from pyomo.environ import *
-import pyomo.contrib.parmest.parmest as parmest
 
-ipopt_available = SolverFactory("ipopt").available()
-kaug_available = SolverFactory("k_aug").available()
-dotsens_available = SolverFactory("dot_sens").available()
+ipopt_available = SolverFactory("ipopt").available(exception_flag=False)
+kaug_available = SolverFactory("k_aug").available(exception_flag=False)
+dotsens_available = SolverFactory("dot_sens").available(exception_flag=False)
 
 
 @pytest.mark.skipif(not ipopt_available, reason="The 'ipopt' command is not available")
@@ -67,7 +70,7 @@ def SSE(model, data):
             rooney_biegler_model, rooney_biegler_model_opt, data, variable_name, SSE
         )
 
-        assert results.obj == approx(4.331711213656886)
+        assert results.obj == pytest.approx(4.331711213656886, abs=1e-8, rel=1e-8)
         np.testing.assert_array_almost_equal(
             results.theta, [19.142575284617866, 0.53109137696521]
         )
@@ -80,7 +83,9 @@ def SSE(model, data):
         np.testing.assert_array_almost_equal(
             results.cov, np.array([[6.30579403, -0.4395341], [-0.4395341, 0.04193591]])
         )
-        assert results.propagation_f == pytest.approx(5.45439337747349)
+        assert results.propagation_f == pytest.approx(
+            5.45439337747349, abs=1e-8, rel=1e-8
+        )
 
     @pytest.mark.component
     def test_quantify_propagate_uncertainty2(self):
@@ -111,14 +116,13 @@ def SSE(model, data):
         model_uncertain.obj = Objective(
             expr=model_uncertain.asymptote
             * (1 - exp(-model_uncertain.rate_constant * 10)),
-            sense=minimize,
         )
 
         results = quantify_propagate_uncertainty(
             rooney_biegler_model, model_uncertain, data, variable_name, SSE
         )
 
-        assert results.obj == approx(4.331711213656886)
+        assert results.obj == pytest.approx(4.331711213656886, abs=1e-8, rel=1e-8)
         np.testing.assert_array_almost_equal(
             results.theta, [19.142575284617866, 0.53109137696521]
         )
@@ -165,7 +169,6 @@ def SSE(model, data):
         model_uncertain.obj = Objective(
             expr=model_uncertain.asymptote
             * (1 - exp(-model_uncertain.rate_constant * 10)),
-            sense=minimize,
         )
 
         propagate_results = propagate_uncertainty(
@@ -215,9 +218,7 @@ def test_propagate_uncertainty1(self):
         m.con2 = Constraint(expr=m.x2 + m.x3 - m.p2 == 0)
 
         # Define objective
-        m.obj = Objective(
-            expr=m.p1 * m.x1 + m.p2 * (m.x2**2) + m.p1 * m.p2, sense=minimize
-        )
+        m.obj = Objective(expr=m.p1 * m.x1 + m.p2 * (m.x2**2) + m.p1 * m.p2)
 
         ### Solve optimization model
         opt = SolverFactory("ipopt", tee=True)
@@ -422,7 +423,6 @@ def SSE(model, data):
         model_uncertain.obj = Objective(
             expr=model_uncertain.asymptote
             * (1 - exp(-model_uncertain.rate_constant * 10)),
-            sense=minimize,
         )
         with pytest.raises(TypeError):
             propagate_results = propagate_uncertainty(1, theta, cov, variable_name)

idaes/config.py

@@ -65,7 +65,7 @@
     "amd64": "x86_64",
     "arm64": "aarch64",
 }
-# Set of extra binary packages and basic build platforom where available
+# Set of extra binary packages and basic build platform where available
 extra_binaries = {
     "petsc": base_platforms,
 }