Merge branch 'OpenMDAO:master' into diag

benchmark/benchmark_balanced_field.py

@@ -20,7 +20,7 @@ def _run_balanced_field_length_problem(tx=dm.GaussLobatto, timeseries=True, sim=
     p.driver.options['optimizer'] = optimizer
     p.driver.options['print_results'] = False
     if optimizer == 'IPOPT':
-        p.driver.opt_settings['print_level'] = 5
+        p.driver.opt_settings['print_level'] = 0
 
     # First Phase: Brake release to V1 - both engines operable
     br_to_v1 = dm.Phase(ode_class=BalancedFieldODEComp,

benchmark/benchmark_racecar.py

@@ -129,7 +129,7 @@ def _run_racecar_problem(transcription, timeseries=False):
     p.driver.opt_settings['compl_inf_tol'] = 1e-3
     p.driver.opt_settings['acceptable_iter'] = 0
     p.driver.opt_settings['tol'] = 1e-3
-    p.driver.opt_settings['print_level'] = 5
+    p.driver.opt_settings['print_level'] = 0
     p.driver.opt_settings['nlp_scaling_method'] = 'gradient-based'  # for faster convergence
     p.driver.opt_settings['alpha_for_y'] = 'safer-min-dual-infeas'
     p.driver.opt_settings['mu_strategy'] = 'monotone'

docs/dymos_book/faq/downstream_analysis.ipynb

@@ -47,7 +47,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# How do connect the outputs of Dymos to a downstream analysis?\n",
+    "# How do I connect the outputs of Dymos to a downstream analysis?\n",
     "\n",
     "One of the design goals of Dymos is to allow the trajectory to be a part of a larger multidisciplinary optimization problem.\n",
     "Sometimes, you may want to take the results from the Dymos trajectory and feed them to some downstream analysis.\n",

docs/dymos_book/faq/upstream_analysis.ipynb

@@ -47,7 +47,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# How do connect the outputs of an upstream analysis as inputs to Dymos?\n",
+    "# How do I connect the outputs of an upstream analysis as inputs to Dymos?\n",
     "\n",
     "One of the design goals of Dymos is to allow the trajectory to be a part of a larger multidisciplinary optimization problem.\n",
     "Sometimes, you may want to take the results of some upstream design and use them as an input to a Dymos trajectory.\n",

docs/dymos_book/features/trajectories/trajectories.ipynb

@@ -224,7 +224,7 @@
     "If we implemented these as parameters within each phase individually, we would need some constraints to ensure that they held the same value within each phase.\n",
     "To avoid this complexity, Dymos Trajectory objects support their own Parameters.\n",
     "\n",
-    "Like their Phase-based counterparts, Trajectory parameters produce may be design variables for the problem or used as inputs to the trajectory from external sources.\n",
+    "Like their Phase-based counterparts, Trajectory parameters may be design variables for the problem or they may be inputs to the trajectory from external sources.\n",
     "\n",
     "When using Trajectory parameters, their values are connected to each phase as an Input Parameter within the Phase.\n",
     "Because ODEs in different phases may have different names for parameters (e.g. 'mass', 'm', 'm_total', etc) Dymos allows the user to specify the targeted ODE parameters on a phase-by-phase basis using the `targets` and `target_params` option.\n",

docs/dymos_book/getting_started/intro_to_dymos/intro_segments.ipynb

@@ -156,7 +156,7 @@
     "\n",
     "## How does the number and order of segments affect the solution?\n",
     "\n",
-    "Obviously, a single third-order polynomial won't be able to fit highly oscillator behavior.\n",
+    "Obviously, a single third-order polynomial won't be able to fit highly oscillatory behavior.\n",
     "In this case, our guess of using four segments (equally spaced) in the phase wasn't quite sufficient.\n",
     "Let's try increasing that number to ten third-order segments."
    ]

dymos/examples/balanced_field/doc/test_doc_balanced_field_length.py

@@ -29,7 +29,7 @@ def test_balanced_field_length_for_docs(self):
         p.driver.options['optimizer'] = optimizer
         p.driver.options['print_results'] = False
         if optimizer == 'IPOPT':
-            p.driver.opt_settings['print_level'] = 5
+            p.driver.opt_settings['print_level'] = 0
             p.driver.opt_settings['derivative_test'] = 'first-order'
 
         # First Phase: Brake release to V1 - both engines operable

dymos/examples/balanced_field/test/test_balanced_field_func_comp.py

@@ -158,7 +158,7 @@ def _run_problem(self, tx):
         p.driver.options['optimizer'] = 'IPOPT'
         p.driver.options['print_results'] = True
 
-        p.driver.opt_settings['print_level'] = 5
+        p.driver.opt_settings['print_level'] = 0
         p.driver.opt_settings['mu_strategy'] = 'adaptive'
 
         p.driver.opt_settings['bound_mult_init_method'] = 'mu-based'

dymos/examples/balanced_field/test/test_balanced_field_length.py

@@ -18,7 +18,7 @@ def _make_problem(self):
 
         p.driver = om.pyOptSparseDriver()
         p.driver.options['optimizer'] = 'IPOPT'
-        p.driver.opt_settings['print_level'] = 5
+        p.driver.opt_settings['print_level'] = 0
         p.driver.opt_settings['derivative_test'] = 'first-order'
 
         p.driver.declare_coloring()

dymos/examples/brachistochrone/doc/test_doc_brachistochrone.py

@@ -310,7 +310,7 @@ def test_brachistochrone_for_docs_coloring_demo_solve_segments(self):
         #
         p = om.Problem(model=om.Group())
         p.driver = om.pyOptSparseDriver(optimizer='IPOPT')
-        p.driver.opt_settings['print_level'] = 4
+        p.driver.opt_settings['print_level'] = 0
         # p.driver.declare_coloring()
 
         #

dymos/examples/brachistochrone/test/test_brachistochrone_solve_segments.py

@@ -27,7 +27,7 @@ def _make_problem(transcription='gauss-lobatto', num_segments=8, transcription_o
     elif optimizer == 'IPOPT':
         p.driver.opt_settings['mu_init'] = 1e-3
         p.driver.opt_settings['max_iter'] = 500
-        p.driver.opt_settings['print_level'] = 5
+        p.driver.opt_settings['print_level'] = 0
         p.driver.opt_settings['nlp_scaling_method'] = 'gradient-based'  # for faster convergence
         p.driver.opt_settings['alpha_for_y'] = 'safer-min-dual-infeas'
         p.driver.opt_settings['mu_strategy'] = 'monotone'