Added notebook with electric circuit

[miguelcocruz]

Related Issue

#202

[ljvmiranda921]

Hi @miguelcocruz ! Thank you so much for your contribution! We will be reviewing this over the weekend or next week and merge this once everything is OK!

Have a good day!

cc: @whzup

[miguelcocruz]

Hey @ljvmiranda921 ! Thank you for the opportunity!

[whzup]

Hey @miguelcocruz thanks for this nice PR 🎊:+1:. I really like it 😃.
I have some additions, though:

• I think it would be prettier to have the function with out kwargs, i.e. instead of:

def cost_function(I, U, R, I_s, v_t = 25.85 / 1000):
    c = abs(U - v_t * np.log(abs(I[:, 0] / I_s)) - R * I[:, 0])
    return c

More like this:

def cost_function(I):
    # Define U, v_t here as constants
    c = abs(U - v_t * np.log(abs(I[:, 0] / I_s)) - R * I[:, 0])
    return c

This way it is clear that it is only a onedimensional optimization. It might even increase the speed of the optimization.

• It would be nice to have a graph with the cost function and a comparison to another method of finding out the current. I made a screen capture of my TI-Nspire with a solve for this purpose:
  
  You can use this one or if you know something that can solve equations in Python that would be even cooler 😉.

• The optimization now takes over 18 seconds which is extremely long for just a small solve operation in a 1-dimensional space. Could you try lowering the number of iterations (100 or even 10-20 if that works) and look if this works, too?

Again thanks for the PR. It's nice to have you as a contributor 😄.

[whzup]

Really nice! 😄 Thank you for your work @miguelcocruz! 💯

[miguelcocruz]

Woohoo, my first PR! 🎊 Thank you @whzup for the suggestions

[ljvmiranda921]

Hey, congratulations @miguelcocruz for your first PR! 🎉 🎉
@all-contributors please add @miguelcocruz for docs and examples

[allcontributors]

@ljvmiranda921

I've put up a pull request to add @miguelcocruz! 🎉