Clean up code flow in doing and returning conventional parameter optimization

[AdityaSavara]

Clean up code flow in doing and returning conventional parameter optimization

We probably don't need to plot the conventional parameter optimization response surface, we can probably just return the final values from BPE versus CPE.

It'd be better to have the CPE plotted also as a point on the same graph as the prior and posterior like in the perspective, but that is not something we will support at this time.

[AdityaSavara]

Should include some form of weighted CPE with error bars returned:
https://stackoverflow.com/questions/21469620/how-to-do-linear-regression-taking-errorbars-into-account

Maybe also allow Levenberg-Marquardt through sicipy optimize root.

https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root.html#scipy.optimize.root

https://mathoverflow.net/questions/257699/gauss-newton-vs-gradient-descent-vs-levenberg-marquadt-for-least-squared-method
"The Levenberg-Marquardt curve-fitting method is actually a combination of the two other minimization methods: the gradient descent method and the Gauss-Newton method. In the gradient descent method, the sum of the squared errors is reduced by updating the parameters in the steepest-descent direction. In the Gauss-Newton method, the sum of the squared errors is reduced by assuming the least squares function is locally quadratic, and finding the minimum of the quadratic. The Levenberg-Marquardt method acts more like a gradient-descent method when the parameters are far from their optimal value, and acts more like the Gauss-Newton method when the parameters are close to their optimal value."