Derivative check with scalers

Question

I have a problem that I want to scale the design variables. I have added the scaler, but I want to check the derivative to make sure it is doing what I want it to do. Is there a way to check the scaled derivative? I have tried to use check_total_derivatives() but the derivative is the exact same regardless of what value I put for scaler:

from openmdao.api import Component, Group, Problem, IndepVarComp, ExecComp
from openmdao.drivers.pyoptsparse_driver import pyOptSparseDriver

class Scaling(Component):
    def __init__(self):
        super(Scaling, self).__init__()
        self.add_param('x', shape=1)
        self.add_output('y', shape=1)

    def solve_nonlinear(self, params, unknowns, resids):
        unknowns['y'] = 1000. * params['x']**2 + 2

    def linearize(self, params, unknowns, resids):
        J = {}
        J['y', 'x'] = 2000. * params['x']
        return J


class ScalingGroup(Group):
    def __init__(self,):
        super(ScalingGroup, self).__init__()
        self.add('x', IndepVarComp('x', 0.0), promotes=['*'])
        self.add('g', Scaling(), promotes=['*'])

p = Problem()
p.root = ScalingGroup()

# p.driver = pyOptSparseDriver()
# p.driver.options['optimizer'] = 'SNOPT'
p.driver.add_desvar('x', lower=0.005, upper=100., scaler=1000)
p.driver.add_objective('y')
p.setup()
p['x'] = 3.

p.run()
total = p.check_total_derivatives()
# Derivative is the same regardless of what the scaler is.

Answer 1

Another way to see exactly what the optimizer is seeing from calc_gradient is to mimic the call to calc_gradient. This is not necessarily easy to figure out, but I thought I would paste it here for reference.

print p.calc_gradient(list(p.driver.get_desvars().keys()),
                      list(p.driver.get_objectives().keys()) + list(p.driver.get_constraints().keys()),
                      dv_scale=p.driver.dv_conversions,
                      cn_scale=p.driver.fn_conversions)