Merge f7e4000 into 60e676d

openmdao/core/problem.py

@@ -309,7 +309,7 @@ def setup(self, vector_class=DefaultVector, check=True, logger=None, mode='auto'
 
     def check_partials(self, logger=None, comps=None, compact_print=False,
                        abs_err_tol=1e-6, rel_err_tol=1e-6, global_options=None,
-                       force_dense=True):
+                       force_dense=True, suppress_output=False):
         """
         Check partial derivatives comprehensively for all components in your model.
 
@@ -334,6 +334,8 @@ def check_partials(self, logger=None, comps=None, compact_print=False,
             'form', 'step', 'step_calc', and 'method' can be specified in this way.
         force_dense : bool
             If True, analytic derivatives will be coerced into arrays.
+        suppress_output : bool
+            Set to True to suppress all output.
 
         Returns
         -------
@@ -593,14 +595,15 @@ def check_partials(self, logger=None, comps=None, compact_print=False,
         partials_data = {comp_name: dict(outer) for comp_name, outer in iteritems(partials_data)}
 
         logging.getLogger().setLevel(logging.INFO)
-        _assemble_derivative_data(partials_data, rel_err_tol, abs_err_tol, logger,
-                                  compact_print, comps, global_options)
+        if not suppress_output:
+            _assemble_derivative_data(partials_data, rel_err_tol, abs_err_tol, logger,
+                                      compact_print, comps, global_options)
 
         return partials_data
 
     def check_total_derivatives(self, of=None, wrt=None, logger=None, compact_print=False,
                                 abs_err_tol=1e-6, rel_err_tol=1e-6, method='fd', step=1e-6,
-                                form='forward', step_calc='abs'):
+                                form='forward', step_calc='abs', suppress_output=False):
         """
         Check total derivatives for the model vs. finite difference.
 
@@ -631,6 +634,8 @@ def check_total_derivatives(self, of=None, wrt=None, logger=None, compact_print=
             Form for finite difference, can be 'forward', 'backward', or 'central'.
         step_calc : string
             Step type for finite difference, can be 'abs' for absolute', or 'rel' for relative.
+        suppress_output : bool
+            Set to True to suppress all output.
 
         Returns
         -------
@@ -682,8 +687,9 @@ def check_total_derivatives(self, of=None, wrt=None, logger=None, compact_print=
         fd_args['method'] = 'fd'
 
         logging.getLogger().setLevel(logging.INFO)
-        _assemble_derivative_data(data, rel_err_tol, abs_err_tol, logger, compact_print,
-                                  [model], fd_args, totals=True)
+        if not suppress_output:
+            _assemble_derivative_data(data, rel_err_tol, abs_err_tol, logger, compact_print,
+                                      [model], fd_args, totals=True)
         return data['']
 
     def compute_total_derivs(self, of=None, wrt=None, return_format='flat_dict'):

openmdao/core/tests/test_check_derivs.py

@@ -105,6 +105,42 @@ def compute_partials(self, inputs, outputs, partials):
         assert_rel_error(self, x2_error.forward, 9., 1e-8)
         assert_rel_error(self, x2_error.reverse, 9., 1e-8)
 
+    def test_feature_check_partials_suppress(self):
+        class MyComp(ExplicitComponent):
+            def setup(self):
+                self.add_input('x1', 3.0)
+                self.add_input('x2', 5.0)
+
+                self.add_output('y', 5.5)
+
+            def compute(self, inputs, outputs):
+                """ Doesn't do much. """
+                outputs['y'] = 3.0*inputs['x1'] + 4.0*inputs['x2']
+
+            def compute_partials(self, inputs, outputs, partials):
+                """Intentionally incorrect derivative."""
+                J = partials
+                J['y', 'x1'] = np.array([4.0])
+                J['y', 'x2'] = np.array([40])
+
+        prob = Problem()
+        prob.model = Group()
+
+        prob.model.add_subsystem('p1', IndepVarComp('x1', 3.0))
+        prob.model.add_subsystem('p2', IndepVarComp('x2', 5.0))
+        prob.model.add_subsystem('comp', MyComp())
+
+        prob.model.connect('p1.x1', 'comp.x1')
+        prob.model.connect('p2.x2', 'comp.x2')
+
+        prob.set_solver_print(level=0)
+
+        prob.setup(check=False)
+        prob.run_model()
+
+        data = prob.check_partials(suppress_output=True)
+        print(data)
+
     def test_component_only(self):
         class MyComp(ExplicitComponent):
             def setup(self):
@@ -145,6 +181,38 @@ def compute_partials(self, inputs, outputs, partials):
         self.assertFalse(lines[y_wrt_x1_line+6].endswith('*'),
                         msg='Error flag not expected in output but displayed')
 
+    def test_component_only_suppress(self):
+        class MyComp(ExplicitComponent):
+            def setup(self):
+                self.add_input('x1', 3.0)
+                self.add_input('x2', 5.0)
+
+                self.add_output('y', 5.5)
+
+            def compute(self, inputs, outputs):
+                """ Doesn't do much. """
+                outputs['y'] = 3.0*inputs['x1'] + 4.0*inputs['x2']
+
+            def compute_partials(self, inputs, outputs, partials):
+                """Intentionally incorrect derivative."""
+                J = partials
+                J['y', 'x1'] = np.array([4.0])
+                J['y', 'x2'] = np.array([40])
+
+        prob = Problem()
+        prob.model = MyComp()
+
+        prob.set_solver_print(level=0)
+
+        prob.setup(check=False)
+        prob.run_model()
+
+        testlogger = TestLogger()
+        data = prob.check_partials(logger=testlogger, suppress_output=True)
+
+        lines = testlogger.get('info')
+        self.assertEqual(len(lines), 0)
+
     def test_missing_entry(self):
         class MyComp(ExplicitComponent):
             def setup(self):
@@ -483,5 +551,33 @@ def test_cs(self):
         assert_rel_error(self, totals['con_cmp2.con2', 'px.x']['J_fwd'], [[0.09692762]], 1e-5)
         assert_rel_error(self, totals['con_cmp2.con2', 'px.x']['J_fd'], [[0.09692762]], 1e-5)
 
+    def test_cs_suppress(self):
+        prob = Problem()
+        prob.model = SellarDerivatives()
+        prob.model.nonlinear_solver = NonlinearBlockGS()
+
+        prob.model.add_design_var('x', lower=-100, upper=100)
+        prob.model.add_design_var('z', lower=-100, upper=100)
+        prob.model.add_objective('obj')
+        prob.model.add_constraint('con1', upper=0.0)
+        prob.model.add_constraint('con2', upper=0.0)
+
+        prob.set_solver_print(level=0)
+
+        prob.setup(force_alloc_complex=True)
+
+        # We don't call run_driver() here because we don't
+        # actually want the optimizer to run
+        prob.run_model()
+
+        # check derivatives with complex step and a larger step size.
+        testlogger = TestLogger()
+        totals = prob.check_total_derivatives(method='cs', step=1.0e-1, logger=testlogger,
+                                              suppress_output=True)
+
+        lines = testlogger.get('info')
+
+        self.assertEqual(len(lines), 0)
+
 if __name__ == "__main__":
     unittest.main()

openmdao/core/tests/test_problem.py

@@ -253,6 +253,27 @@ def test_feature_check_total_derivatives_from_driver(self):
         # check derivatives of all obj+constraints w.r.t all design variables
         prob.check_total_derivatives()
 
+    def test_feature_check_total_derivatives_suppress(self):
+        prob = Problem()
+        prob.model = SellarDerivatives()
+        prob.model.nonlinear_solver = NonlinearBlockGS()
+
+        prob.model.add_design_var('x', lower=-100, upper=100)
+        prob.model.add_design_var('z', lower=-100, upper=100)
+        prob.model.add_objective('obj')
+        prob.model.add_constraint('con1', upper=0.0)
+        prob.model.add_constraint('con2', upper=0.0)
+
+        prob.setup()
+
+        # We don't call run_driver() here because we don't
+        # actually want the optimizer to run
+        prob.run_model()
+
+        # check derivatives of all obj+constraints w.r.t all design variables
+        totals = prob.check_total_derivatives(suppress_output=True)
+        print(totals)
+
     def test_feature_check_total_derivatives_cs(self):
         prob = Problem()
         prob.model = SellarDerivatives()

openmdao/docs/features/defining_partials/checking_partials.rst

@@ -19,4 +19,15 @@ Usage
 1. When the difference between the FD derivative and the provided derivative is larger (in either a relative or absolute sense) than :code:`1e-6`, that partial derivative will be marked with a :code:`'*'`.
 
 .. embed-test::
-    openmdao.core.tests.test_check_derivs.TestProblemCheckPartials.test_feature_incorrect_jacobian
+    openmdao.core.tests.test_check_derivs.TestProblemCheckPartials.test_feature_incorrect_jacobian
+
+----
+
+Turn off standard output and just view the derivatives in the return:
+
+.. embed-test::
+    openmdao.core.tests.test_check_derivs.TestProblemCheckPartials.test_feature_check_partials_suppress
+
+Related Features
+-----------------
+check-total-derivatives

openmdao/docs/features/running/check_total_derivatives.rst

@@ -45,6 +45,13 @@ step size to trigger the nonlinear Gauss-Seidel solver to try to converge after
 .. embed-test::
     openmdao.core.tests.test_problem.TestProblem.test_feature_check_total_derivatives_cs
 
+----
+
+Turn off standard output and just view the derivatives in the return:
+
+.. embed-test::
+    openmdao.core.tests.test_problem.TestProblem.test_feature_check_total_derivatives_suppress
+
 
 Related Features
 -----------------