import matplotlib.pyplot as plt
%matplotlib inline

from controlgame import ControlGame

game = ControlGame(runtime=30)  # seconds

1. Instructions

Run the cell below and click the “run” button. Then move the “MV” slider in a way which gets the controlled slider close to the setpoint. Your score increases more quickly when Controlled is near Setpoint. See how high your score can get by clicking run a couple of times. To see your performance graphed out, execute the next cell (game.plot())

game.ui()

game.plot()

import scipy.signal

ts = game.ts

G = scipy.signal.lti(2, [2, 0])

import numpy

LIMIT = 100

def score(ts, sps, cvs):
    scores = 1 - numpy.minimum(numpy.abs(numpy.array(sps) - numpy.array(cvs)), LIMIT)/LIMIT

    score = sum(scores)

    return score

def sim(ts, mvs):
    _, cvs, _ = scipy.signal.lsim(G, mvs, ts)

    return cvs

def objective(mvs):
    return -score(game.ts, game.sps, sim(game.ts, mvs))

objective(game.mvs)

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-15-c61c532d40ad> in <module>
----> 1 objective(game.mvs)

<ipython-input-13-96e53751337c> in objective(mvs)
      1 def objective(mvs):
----> 2     return -score(game.ts, game.sps, sim(game.ts, mvs))

<ipython-input-12-d6eb717acf80> in sim(ts, mvs)
      1 def sim(ts, mvs):
----> 2     _, cvs, _ = scipy.signal.lsim(G, mvs, ts)
      3
      4     return cvs

~/anaconda3/lib/python3.7/site-packages/scipy/signal/ltisys.py in lsim(system, U, T, X0, interp)
   1944     xout = zeros((n_steps, n_states), sys.A.dtype)
   1945
-> 1946     if T[0] == 0:
   1947         xout[0] = X0
   1948     elif T[0] > 0:

IndexError: index 0 is out of bounds for axis 0 with size 0

import scipy.optimize

guesses = 1

bestmvs = game.mvs
for i in range(guesses):
    sol = scipy.optimize.minimize(objective, bestmvs + 2*(numpy.random.rand(len(bestmvs))*2-1), bounds=[(-LIMIT, LIMIT)]*len(game.mvs))
    print('Score:', -sol.fun)
    bestmvs = sol.x
    bestmvs[numpy.abs(bestmvs)<10] = 0

bestcvs = sim(ts, bestmvs)

fig, (axmv, axcv) = plt.subplots(2, 1)
axmv.plot(ts, bestmvs)
axcv.plot(ts, game.sps, ts, bestcvs)