Simple Explicit Expressions

In this example, we define some variables that are computed explicitly, and regiser those variables as outputs. Each output in this example is computed explicitly using the basic mathematical operations. The example below is described as follows.

Model.declare_variable returns a Variable object, which represents a value that comes from outside the current model, either as an output of a parent model (a model that is above the current model in the model hierarchy), or an output of a child model (a model that is below the current model in the model hierarchy).

Some variables are defined using mathematical expressions, and finally, each output is registered. To register an output of the model, use the Model.register_output method. Model.register_output requires a name that the CSDL compiler back end will use to access the variable output. This name does not need to be the same as the Python variable name, but it's good practice to stay consistent. The CSDL compiler back end has no knowledge of the Python variable name, only the name provided in methods like Model.create_input, Model.declare_variable, and Model.register_output.

from csdl_om import Simulatorfrom csdl import Model, NonlinearBlockGSimport csdlimport numpy as np

class ExampleBinaryOperations(Model):
    def define(self):        # declare inputs with default values        x1 = self.declare_variable('x1', val=2)        x2 = self.declare_variable('x2', val=3)        x3 = self.declare_variable('x3', val=np.arange(7))
        # Expressions with multiple binary operations        y1 = -2 * x1**2 + 4 * x2 + 3        self.register_output('y1', y1)
        # Elementwise addition        y2 = x2 + x1
        # Elementwise subtraction        y3 = x2 - x1
        # Elementwise multitplication        y4 = x1 * x2
        # Elementwise division        y5 = x1 / x2        y6 = x1 / 3        y7 = 2 / x2
        # Elementwise Power        y8 = x2**2        y9 = x1**2
        self.register_output('y2', y2)        self.register_output('y3', y3)        self.register_output('y4', y4)        self.register_output('y5', y5)        self.register_output('y6', y6)        self.register_output('y7', y7)        self.register_output('y8', y8)        self.register_output('y9', y9)
        # Adding other expressions        self.register_output('y10', y1 + y7)
        # Array with scalar power        y11 = x3**2        self.register_output('y11', y11)
        # Array with array of powers        y12 = x3**(2 * np.ones(7))        self.register_output('y12', y12)

sim = Simulator(ExampleBinaryOperations())sim.run()
print('y1', sim['y1'].shape)print(sim['y1'])print('y2', sim['y2'].shape)print(sim['y2'])print('y3', sim['y3'].shape)print(sim['y3'])print('y4', sim['y4'].shape)print(sim['y4'])print('y5', sim['y5'].shape)print(sim['y5'])print('y6', sim['y6'].shape)print(sim['y6'])print('y7', sim['y7'].shape)print(sim['y7'])print('y8', sim['y8'].shape)print(sim['y8'])print('y9', sim['y9'].shape)print(sim['y9'])print('y10', sim['y10'].shape)print(sim['y10'])print('y11', sim['y11'].shape)print(sim['y11'])print('y12', sim['y12'].shape)print(sim['y12'])

[7.]y2 (1,)[5.]y3 (1,)[1.]y4 (1,)[6.]y5 (1,)[0.66666667]y6 (1,)[0.66666667]y7 (1,)[0.66666667]y8 (1,)[9.]y9 (1,)[4.]y10 (1,)[7.66666667]y11 (7,)[ 0.  1.  4.  9. 16. 25. 36.]y12 (7,)[ 0.  1.  4.  9. 16. 25. 36.]