In the previous tutorial, we discussed the three basic kinds of Components in the OpenMDAO framework. This tutorial focuses on using one of those, ExplicitComponent, to build a simple analysis of a paraboloid function. We’ll explain the basic structure of a run file, show you how to set inputs, run the model, and check the output files.

# Paraboloid - A Single-Discipline Model¶

Consider a paraboloid, defined by the explicit function

$f(x,y) = (x-3.0)^2 + x \times y + (y+4.0)^2 - 3.0 ,$

where $$x$$ and $$y$$ are the inputs to the function. The minimum of this function is located at

$x = \frac{20}{3} \quad , \quad y = -\frac{22}{3} .$

Here is a complete script that defines this equation as a component and then executes it with different input values, printing the results to the console when it’s done. Take a look at the full run script first, then we’ll break it down part by part to explain what each one does.

"""
Demonstration of a model using the Paraboloid component.
"""
from __future__ import division, print_function
from openmdao.api import ExplicitComponent

class Paraboloid(ExplicitComponent):
"""
Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3.
"""

def setup(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

if __name__ == "__main__":
from openmdao.core.problem import Problem
from openmdao.core.group import Group
from openmdao.core.indepvarcomp import IndepVarComp

model = Group()
ivc = IndepVarComp()

model.connect('des_vars.x', 'parab_comp.x')
model.connect('des_vars.y', 'parab_comp.y')

prob = Problem(model)
prob.setup()
prob.run_model()
print(prob['parab_comp.f_xy'])

prob['des_vars.x'] = 5.0
prob['des_vars.y'] = -2.0
prob.run_model()
print(prob['parab_comp.f_xy'])


Next, let’s break this script down and understand each section:

## Preamble¶

from __future__ import division, print_function
from openmdao.api import ExplicitComponent


At the top of any script you’ll see these lines (or lines very similar to these) which import needed classes and functions. On the first import line, the print_function is used so that the code in the script will work in either Python 2 or 3. If you want to know whats going on with the division operator, check out this detailed explanation.

The second import line brings in OpenMDAO classes that are needed to build and run a model. As you progress to more complex models, you can expect to import more classes from openmdao.api, but for now we only need this one to define our paraboloid component.

## Defining a Component¶

The component is the basic building block of a model. You will always define components as a subclass of either ExplicitComponent or ImplicitComponent. Since our simple paraboloid function is explicit, we’ll use the ExplicitComponent. You see two methods defined:

• setup: define all your inputs and outputs here, and declare derivatives
• compute: calculation of all output values for the given inputs

In the setup method you define the inputs and outputs of the component, and in this case you also ask OpenMDAO to approximate all the partial derivatives (derivatives of outputs with respect to inputs) using the finite difference method.

Note

One of OpenMDAO’s most unique features is its support for analytic derivatives. Providing analytic partial derivatives from your components can result in much more efficient optimizations. We’ll get to using analytic derivatives in later tutorials.

class Paraboloid(ExplicitComponent):
"""
Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3.
"""

def setup(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


## The Run Script¶

In this example we’ve set up the run script at the bottom of the file. The start of the run script is denoted by the following statement:

if __name__ == '__main__':

At the top of our run script, we import the remaining OpenMDAO classes that we will need to define our problem.

All OpenMDAO models are built up from a hierarchy of Group instances that organize the components. In this example, the hierarchy is very simple, consisting of a single root group that holds two components. The first component is an IndepVarComp instance. This is a special component that OpenMDAO provides for you to specify the independent variables in your problem. The second component is an instance of the Paraboloid class that we just defined.

As part of the model hierarchy, you will also define any connections to move data between components in the relevant group. Here, we connect the independent variables to the inputs on the paraboloid component.

Once the model hierarchy is defined, we pass it to the constructor of the Problem class. Then we call the setup() method on that problem, which tells the framework to do some initial work to get the data structures in place for execution. In this case, we call run_model() to actually perform the computation. Later, we’ll see how to explicitly set drivers and will be calling run_driver() instead.

Here we called run_model twice. The first time x and y have the initial values of 3.0 and -4.0 respectively. The second time we changed those values and then re-ran. There are a few details to note here. First, notice the way we printed the outputs via prob['parab_comp.f_xy'] and similarly how we set the new values for x and y. You can both get and set values using the problem, which works with dimensional values in the units of the source variable. In this case, there are no units on the source (i.e. des_vars.x).

Note

Detailed information on units and scaling can be found in the feature documentation.

if __name__ == "__main__":
from openmdao.api import Problem
from openmdao.api import Group
from openmdao.api import IndepVarComp

model = Group()
ivc = IndepVarComp()

model.connect('des_vars.x', 'parab_comp.x')
model.connect('des_vars.y', 'parab_comp.y')

prob = Problem(model)
prob.setup()
prob.run_model()
print(prob['parab_comp.f_xy'])

prob['des_vars.x'] = 5.0
prob['des_vars.y'] = -2.0
prob.run_model()
print(prob['parab_comp.f_xy'])