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+POEM ID: 101  
+Title: Input checking utility  
+authors: lamkina (Andrew Lamkin)  
+Competing POEMs:  
+Related POEMs:  
+Associated implementation PR: N/A  
+
+Status:
+
+- [x] Active
+- [ ] Requesting decision
+- [ ] Accepted
+- [ ] Rejected
+- [ ] Integrated
+
+
+## Motivation
+Setting up and running OpenMDAO models often involves changing the default value of component inputs.
+As models grow in complexity, the number of inputs grows rapidly, quickly increasing the difficulty of tracking default vs. user-set inputs.
+Currently, there is not a utility that compares the default component inputs against user-set inputs with a filterable report.
+This POEM proposes to create a utility to check the inputs before the `run_model` call to clearly expose the default and user-set inputs for debugging.
+
+## Description
+I suggest a utility that caches the default input values as metadata upon creation of a component.
+Then, the utility will query the cache right before `run_model` and compare the current value of each input against the cache value.
+Using that information, the utility outputs a report showing the user-set inputs and highlights any inputs that retained a default value.
+The report will adopt the html format of the `check_connections` utility and offer similar filter capability, as well as a color scheme for quickly identifying the source of each input value.
+
+To establish a color scheme, I split the input values into three categories:
+1. Default inputs: These are established when `setup` is called on the component.
+2. Intermediate inputs: These are changed by a group above the component during the `setup` or `configure` phases.
+3. User inputs: These are changed by the user after `setup` and `configure`, but before `run_model`.
+
+I suggest white to signify default inputs, yellow for intermediate inputs, and blue for user inputs.
+
+I also suggest including detailed filtering capability for the html report.
+The filter should have the following functionality:
+1. Filter by exact variable path name.
+2. Filter by glob pattern.
+3. Filter by input category (listed above).
+4. Filter by input value.
+5. Filter by input for implicit or explicit components.
+
+This utility can be a command line tool `openmdao check_inputs <script.py>`, or an optional feature that can be turned-on during the `setup` call with an argument of `setup(check="inputs")`
+
+## Example
+
+In the following example, the interface change is minimal, with only an optional `check="inputs"` argument added to the `setup` call.
+I added comments to show the delineation between `Default`, `Intermediate`, and `User` inputs for this case.
+The `ExampleGroup` is only included to show the `Intermediate` inputs and is not used in the script.
+The result of running this script with `check="inputs"` would be an html report that is similar to existing OpenMDAO reporting formats.
+
+```python
+class Paraboloid(om.ExplicitComponent):
+    """
+    Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3.
+    """
+
+    def setup(self):
+        self.add_input('x', val=0.0) # Default inputs
+        self.add_input('y', val=0.0) # Default inputs
+
+        self.add_output('f_xy', val=0.0)
+
+    def setup_partials(self):
+        # Finite difference all partials.
+        self.declare_partials('*', '*', method='fd')
+
+    def compute(self, inputs, outputs):
+        """
+        f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3
+
+        Minimum at: x = 6.6667; y = -7.3333
+        """
+        x = inputs['x']
+        y = inputs['y']
+
+        outputs['f_xy'] = (x - 3.0)**2 + x * y + (y + 4.0)**2 - 3.0
+
+#######################################################################################
+# Included only for illustrating the intermediate inputs (not used in the script below)
+#######################################################################################
+class ExampleGroup(om.Group);
+    def setup(self):
+        self.add_subsystem("parab_comp", Paraboloid())
+        self.set_input_default("x", val=10.0) # Intermediate input
+
+if __name__ == "__main__":
+    model = om.Group()
+    model.add_subsystem('parab_comp', Paraboloid())
+
+    prob = om.Problem(model)
+    prob.setup(check="inputs") # Input checking turned on
+
+    prob.set_val('parab_comp.x', 3.0)   # User inputs
+    prob.set_val('parab_comp.y', -4.0)  # User inputs
+
+    prob.run_model()
+    print(prob['parab_comp.f_xy'])
+
+    prob.set_val('parab_comp.x', 5.0)   # User inputs
+    prob.set_val('parab_comp.y', -2.0)  # User inputs
+
+    prob.run_model()
+    print(prob.get_val('parab_comp.f_xy'))
+```
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