Merge 0955a53 into a855771

openmdao/code_review/test_lint_attributes.py

@@ -69,7 +69,7 @@ def test_attributes(self):
 
             # Loop over files
             for file_name in os.listdir(dirpath):
-                if file_name != '__init__.py' and file_name[-3:] == '.py':
+                if not file_name.startswith('_') and file_name[-3:] == '.py':
                     if print_info:
                         print('File: {}'.format(file_name))
 

openmdao/code_review/test_lint_docstrings.py

@@ -527,7 +527,7 @@ def test_docstrings(self):
 
             # Loop over files
             for file_name in os.listdir(dirpath):
-                if file_name != '__init__.py' and file_name[-3:] == '.py' and not os.path.isdir(file_name):
+                if not file_name.startswith("_") and file_name[-3:] == '.py' and not os.path.isdir(file_name):
                     if print_info:
                         print(file_name)
 

openmdao/code_review/test_lint_peps.py

@@ -27,7 +27,7 @@
 
 file_excludes = [
     'test_*',
-    '__init__.py',
+    '_*.py',
 ]
 
 ignores = {

openmdao/docs/features/model_visualization/index.rst

@@ -15,3 +15,4 @@ a model.
     view_connections.rst
     n2_basics.rst
     n2_details.rst
+    meta_model_basics.rst

openmdao/docs/features/model_visualization/meta_model_basics.rst

@@ -0,0 +1,38 @@
+.. _meta_model_basics:
+
+*************************
+Metamodel Visualization
+*************************
+
+When evaluating surrogate models, it can be helpful to determine training data fit graphically. OpenMDAO
+has created a package to visualize the training data and surrogate models generated from it. This page
+explains how to use the `meta_model_viewer.py` package from the command line.
+
+The metamodel viewer allows a user the ability of reducing a high dimentional input space down
+to three dimensions to enable the user to determine the fit of a surrogate model to the given
+training data.
+
+.. embed-code::
+    ../test_suite/test_examples/meta_model_examples/meta_model_viewer_example.py
+
+
+From the Command Line
+---------------------
+
+.. _om-command-view_meta_model:
+
+Generating a metamodel diagram for a model from the command line is easy. You need a
+Python script that contains the metamodel.
+
+Running :code:`openmdao meta_model meta_model_viewer_example.py` will open the metamodel generated from the script in the
+browser and generate a metamodel viewer like the one below.
+
+.. image:: images/meta_model_viewer.png
+   :width: 900
+
+The command, :code:`openmdao meta_model` requires a file path and the name of the surrogate model which you
+want to visualize if there is more than one surrogate in your file:
+
+.. embed-shell-cmd::
+    :cmd: openmdao meta_model -h
+

openmdao/test_suite/test_examples/meta_model_examples/meta_model_viewer_example.py

@@ -0,0 +1,34 @@
+"""Example script for meta model viewer."""
+import numpy as np
+import openmdao.api as om
+from openmdao.visualization.meta_model_viewer.meta_model_visualization import MetaModelVisualization
+
+# Model
+interp = om.MetaModelUnStructuredComp()
+
+# Training Data
+x_train1 = np.linspace(0, 10, 20)
+x_train2 = np.linspace(0, 20, 20)
+x_train3 = np.linspace(0, 30, 20)
+x_train4 = np.linspace(0, 40, 20)
+y_train = np.linspace(10, 20, 20)
+
+# Inputs
+interp.add_input('input_1', 0., training_data=x_train1)
+interp.add_input('input_2', 0., training_data=x_train2)
+interp.add_input('input_3', 0., training_data=x_train3)
+interp.add_input('input_4', 0., training_data=x_train4)
+
+# Outputs
+interp.add_output('output_1', 0., training_data=.5 * np.cos(y_train))
+interp.add_output('output_2', 0., training_data=.5 * np.sin(y_train))
+
+# Surrogate Model
+interp.options['default_surrogate'] = om.ResponseSurface()
+
+prob = om.Problem()
+prob.model.add_subsystem('interp', interp)
+prob.setup()
+prob.final_setup()
+
+viz = MetaModelVisualization(interp)

openmdao/test_suite/test_examples/meta_model_examples/slinear_meta_model_viewer_example.py

@@ -0,0 +1,37 @@
+import numpy as np
+import openmdao.api as om
+from openmdao.visualization.meta_model_viewer.meta_model_visualization import MetaModelVisualization
+
+num_train = 10
+
+x0_min, x0_max = -5.0, 10.0
+x1_min, x1_max = 0.0, 15.0
+train_x0 = np.linspace(x0_min, x0_max, num_train)
+train_x1 = np.linspace(x1_min, x1_max, num_train)
+t_data = np.array([[308.12909601, 253.61567418, 204.6578079, 161.25549718, 123.40874201, 91.1175424,   64.38189835,  43.20180985,  27.5772769,   17.50829952],
+                [162.89542418, 123.20470795,  89.06954726,  60.48994214,  37.46589257, 19.99739855,   8.08446009,   1.72707719,   0.92524984,   5.67897804,],
+                [ 90.2866907,   63.02637433,  41.32161352,  25.17240826,  14.57875856, 9.54066442,  10.05812583,  16.13114279,  27.75971531,  44.94384339,],
+                [ 55.60211264,  38.37989042,  26.71322375,  20.60211264,  20.04655709, 25.04655709,  35.60211264,  51.71322375,  73.37989042, 100.60211264],
+                [ 22.81724065,  13.24080685,   9.2199286,   10.75460591,  17.84483877, 30.49062719, 48.69197117,  72.4488707,  101.76132579, 136.62933643],
+                [  5.11168719,   0.78873608,   2.02134053,   8.80950054,  21.1532161, 39.05248721,  62.50731389,  91.51769611, 126.0836339,  166.20512723],
+                [ 14.3413983,   12.87962416,  16.97340558,  26.62274256,  41.82763509, 62.58808317,  88.90408682, 120.77564601, 158.20276077, 201.18543108],
+                [ 20.18431209,  19.1914092,   23.75406186,  33.87227009,  49.54603386, 70.77535319,  97.56022808, 129.90065853, 167.79664453, 211.24818608],
+                [  8.48953212,   5.57319475,   8.21241294,  16.40718668,  30.15751598, 49.46340083,  74.32484124, 104.74183721, 140.71438873, 182.2424958 ],
+                [ 10.96088904,   3.72881146,   2.05228945,   5.93132298,  15.36591208, 30.35605673,  50.90175693,  77.00301269, 108.65982401, 145.87219088]])
+
+prob = om.Problem()
+ivc = om.IndepVarComp()
+ivc.add_output('x0', 0.0)
+ivc.add_output('x1', 0.0)
+
+prob.model.add_subsystem('p', ivc, promotes=['*'])
+mm = prob.model.add_subsystem('mm', om.MetaModelStructuredComp(method='slinear'),
+                            promotes=['x0', 'x1'])
+mm.add_input('x0', 0.0, train_x0)
+mm.add_input('x1', 0.0, train_x1)
+mm.add_output('f', 0.0, t_data)
+
+prob.setup()
+prob.final_setup()
+
+MetaModelVisualization(mm)

openmdao/utils/om.py

@@ -12,6 +12,9 @@
 from openmdao.core.problem import Problem
 from openmdao.visualization.n2_viewer.n2_viewer import n2
 from openmdao.visualization.connection_viewer.viewconns import view_connections
+from openmdao.components.meta_model_unstructured_comp import MetaModelUnStructuredComp
+from openmdao.components.meta_model_structured_comp import MetaModelStructuredComp
+from openmdao.visualization.meta_model_viewer.meta_model_visualization import view_metamodel
 from openmdao.devtools.debug import config_summary, tree, dump_dist_idxs
 from openmdao.devtools.itrace import _itrace_exec, _itrace_setup_parser
 from openmdao.devtools.iprofile_app.iprofile_app import _iprof_exec, _iprof_setup_parser
@@ -236,6 +239,78 @@ def _viewconns(prob):
     return _viewconns
 
 
+def _meta_model_parser(parser):
+    """
+    Set up the openmdao subparser for the 'openmdao meta_model' command.
+
+    Parameters
+    ----------
+    parser : argparse subparser
+        The parser we're adding options to.
+    """
+    parser.add_argument('file', nargs=1, help='Python file containing the model.')
+    parser.add_argument('-m', '--metamodel_pathname', action='store', dest='pathname',
+                        help='pathname of the metamodel component.')
+    parser.add_argument('-p', '--port_number', default=5007, action='store', dest='port_number',
+                        help='Port number to open viewer')
+
+
+def _meta_model_cmd(options):
+    """
+    Return the post_setup hook function for 'openmdao meta_model'.
+
+    Parameters
+    ----------
+    options : argparse Namespace
+        Command line options.
+
+    Returns
+    -------
+    function
+        The post-setup hook function.
+    """
+    def _view_metamodel(prob):
+        Problem._post_setup_func = None
+
+        mm_types = (MetaModelStructuredComp, MetaModelUnStructuredComp)
+
+        pathname = options.pathname
+        port_number = options.port_number
+
+        if pathname:
+            comp = prob.model._get_subsystem(pathname)
+            if comp and isinstance(comp, mm_types):
+                view_metamodel(comp, port_number)
+                return
+        else:
+            comp = None
+
+        metamodels = {mm.pathname: mm for
+                      mm in prob.model.system_iter(include_self=True, typ=mm_types)}
+
+        mm_names = list(metamodels.keys())
+        mm_count = len(mm_names)
+
+        if mm_count == 0:
+            print("No Metamodel components found in model.")
+
+        elif mm_count == 1 and not pathname:
+            comp = metamodels[mm_names[0]]
+            view_metamodel(comp, port_number)
+
+        else:
+            try_str = "Try one of the following: {}.".format(mm_names)
+
+            if not pathname:
+                raise AttributeError("Metamodel not specified.\n {}".format(try_str))
+            elif not comp:
+                raise AttributeError("Metamodel '{}' not found.\n {}".format(pathname, try_str))
+            else:
+                raise AttributeError("'{}' is not a Metamodel.\n {}".format(pathname, try_str))
+
+    return _view_metamodel
+
+
 def _config_summary_setup_parser(parser):
     """
     Set up the openmdao subparser for the 'openmdao summary' command.
@@ -504,6 +579,7 @@ def _post_setup_exec(options):
              'Print citations referenced by problem'),
     'check': (_check_config_setup_parser, _check_config_cmd,
               'Perform a number of configuration checks on the problem.'),
+    'meta_model': (_meta_model_parser, _meta_model_cmd, "Meta Model Viewer.")
 }
 
 
@@ -527,7 +603,7 @@ def _post_setup_exec(options):
                       'Colored jacobian viewer.'),
     'xdsm': (_xdsm_setup_parser, _xdsm_cmd, 'XDSM viewer.'),
     'scaffold': (_scaffold_setup_parser, _scaffold_exec,
-                 'Generate a simple scaffold for a component.')
+                 'Generate a simple scaffold for a component.'),
 }