Merge pull request #1077 from hschilling/P168758070-show-xy-values

P168758070 Show x/y values for scatter plot training points

openmdao/docs/conf.py

@@ -127,6 +127,7 @@
     'test_suite.scripts',
     'vectors',
     'utils',
+    'visualization',
 ]
 
 if os.path.isfile("make_sourcedocs"):

openmdao/docs/features/model_visualization/meta_model_basics.rst

@@ -51,6 +51,20 @@ run :code:`openmdao view_mm unstructured_meta_model_example.py` in the command l
     in depth overview of what :ref:`Unstructured <feature_MetaModelUnStructuredComp>` and :ref:`Structured <feature_MetaModelStructuredComp>`
     metamodels are.
 
+Multiple Meta Models in Script
+-------------------------------------
+If your model has multiple metamodels, you can specify which of them you want to visualize. For example, in this code
+there are two metamodels.
+
+.. embed-code::
+    ../visualization/meta_model_viewer/tests/multiple_metamodels.py
+
+To visualize only the first one, you would use the command:
+
+.. code::
+
+    openmdao view_mm -m cos_mm multiple_metamodels.py
+
 Command Line Interface
 ----------------------
 

openmdao/visualization/meta_model_viewer/meta_model_visualization.py

@@ -10,7 +10,7 @@
 from bokeh.io import curdoc
 from bokeh.layouts import row, column
 from bokeh.plotting import figure
-from bokeh.models import Slider, ColumnDataSource
+from bokeh.models import Slider, ColumnDataSource, HoverTool
 from bokeh.models import ColorBar, BasicTicker, LinearColorMapper, Range1d
 from bokeh.models.widgets import TextInput, Select
 from bokeh.server.server import Server
@@ -498,11 +498,24 @@ def _contour_data(self):
         if len(data):
             # Add training data points overlay to contour plot
             data = np.array(data)
-            self.contour_training_data_source.data = dict(x=data[:, 0], y=data[:, 1])
-            self.contour_plot.circle(
+            if self.is_structured_meta_model:
+                self.contour_training_data_source.data = dict(x=data[:, 0], y=data[:, 1],
+                                                              z=self.meta_model.training_outputs[
+                                                              self.output_select.value].flatten())
+            else:
+                self.contour_training_data_source.data = dict(x=data[:, 0], y=data[:, 1],
+                                                              z=self.meta_model._training_output[
+                                                              self.output_select.value])
+
+            training_data_renderer = self.contour_plot.circle(
                 x='x', y='y', source=self.contour_training_data_source,
                 size=5, color='white', alpha=0.50)
 
+            self.contour_plot.add_tools(HoverTool(renderers=[training_data_renderer], tooltips=[
+                (self.x_input_select.value + " (train)", '@x'),
+                (self.y_input_select.value + " (train)", '@y'),
+                (self.output_select.value + " (train)", '@z'), ]))
+
         return self.contour_plot
 
     def _right_plot(self):
@@ -560,6 +573,14 @@ def _right_plot(self):
         self.right_alphas = 1.0 - data[:, 2] / self.dist_range
 
         # Training data scatter plot
+        scatter_renderer = right_plot_fig.scatter(x=data[:, 3], y=data[:, 1], line_color=None,
+                                                  fill_color='#000000',
+                                                  fill_alpha=self.right_alphas.tolist())
+
+        right_plot_fig.add_tools(HoverTool(renderers=[scatter_renderer], tooltips=[
+            (self.output_select.value + " (train)", '@x'),
+            (y_idx + " (train)", '@y'),
+        ]))
         right_plot_fig.scatter(x=data[:, 3], y=data[:, 1], line_color=None, fill_color='#000000',
                                fill_alpha=self.right_alphas.tolist())
 
@@ -627,8 +648,14 @@ def _bottom_plot(self):
         self.bottom_alphas = 1.0 - data[:, 2] / self.dist_range
 
         # Training data scatter plot
-        bottom_plot_fig.scatter(x=data[:, 0], y=data[:, 3], line_color=None, fill_color='#000000',
-                                fill_alpha=self.bottom_alphas.tolist())
+        scatter_renderer = bottom_plot_fig.scatter(x=data[:, 0], y=data[:, 3], line_color=None,
+                                                   fill_color='#000000',
+                                                   fill_alpha=self.bottom_alphas.tolist())
+
+        bottom_plot_fig.add_tools(HoverTool(renderers=[scatter_renderer], tooltips=[
+            (x_idx + " (train)", '@x'),
+            (self.output_select.value + " (train)", '@y'),
+        ]))
 
         # Set the right_plot data source to new values
         self.bottom_plot_scatter_source.data = dict(

openmdao/visualization/meta_model_viewer/tests/multiple_metamodels.py

@@ -0,0 +1,48 @@
+import numpy as np
+import openmdao.api as om
+
+
+class CosMetaModel(om.MetaModelUnStructuredComp):
+    def setup(self):
+        # Training Data
+        x_train = np.linspace(0, 10, 20)
+        y_train = np.linspace(0, 20, 20)
+
+        # Inputs
+        self.add_input('x', 0., training_data=x_train)
+        self.add_input('y', 0., training_data=y_train)
+
+        # Outputs
+        self.add_output('cos_x', 0., training_data=np.cos(x_train + y_train))
+
+        # Surrogate Model
+        self.options['default_surrogate'] = om.ResponseSurface()
+
+
+class SinMetaModel(om.MetaModelUnStructuredComp):
+    def setup(self):
+        # Training Data
+        x_train = np.linspace(0, 10, 20)
+        y_train = np.linspace(0, 20, 20)
+
+        # Inputs
+        self.add_input('x', 0., training_data=x_train)
+        self.add_input('y', 0., training_data=y_train)
+
+        # Outputs
+        self.add_output('sin_x', 0., training_data=np.sin(x_train + y_train))
+
+        # Surrogate Model
+        self.options['default_surrogate'] = om.ResponseSurface()
+
+
+# define model with two metamodel components
+model = om.Group()
+cos_mm = model.add_subsystem('cos_mm', CosMetaModel())
+sin_mm = model.add_subsystem('sin_mm', SinMetaModel())
+
+# setup a problem using our dual metamodel model
+prob = om.Problem(model)
+prob.setup()
+prob.final_setup()
+