import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import RiseFallSVM as svm
from BrakeController import Controller
from xyFit import *
test = 'PG188Test9.csv'
trials = 100
atrials = 350
(data, BrakeStrength) = collect(trials, test, atrials=atrials)
data = np.loadtxt('data/' + test, delimiter=',', comments='# ')
BrakeStrength = np.loadtxt('data/BrakeCommands' + test,
delimiter=',',
comments='# ')
compData = arrange(data, BrakeStrength, test)
# plt.plot(BrakeStrength)
# plt.show()
# compData = np.loadtxt('data/Comp' + test, delimiter=',', comments='#')
# fig = plt.figure()
# ax0 = fig.add_subplot(111, projection='3d')
# print(np.shape(compData))
# ax0.scatter(compData[:, 0], compData[:, 1], compData[:, 2],
# c=compData[:, 0] - compData[:, 1], depthshade=False)
# plt.title('Brake Commands Vs. Torque')
# ax0.set_xlabel('Brake Command (%)')
# ax0.set_ylabel(' Prev Brake Command (%)')
# ax0.set_zlabel('Torque (in-lb)')
# ax0 = fig.add_subplot(111, projection='3d')
(data, brakeStrength) = collect(brakeStrength,
currdir,
test,
timeLength=10,
mode='warmup',
breed=breed,
stepwise=True)
data = np.loadtxt(currdir + test + '.csv', delimiter=',', comments='# ')
brakeStrength = np.loadtxt(currdir + 'BrakeCommands' + test + '.csv',
delimiter=',',
comments='# ')
# arrange brake Data
compData = arrange(
data,
brakeStrength,
currdir,
test,
)
# import curve information
with open(paramdir + 'curveP.pickle', 'rb') as g:
(riseP, fallP) = pickle.load(g)
tumInd = 0
toFallTable = []
toRiseTable = []
# parse data into sets of tumbles based on index
for tum in torqueList:
# use curve information to determine linear points
dna_pts = []
# plt.legend()
plt.title('Speed Vs. Time')
plt.ylabel('Speed (%)')
plt.xlabel('Time(s)')
ax2 = plt.subplot(414)
ax2.plot(time,
data[:, 5])
plt.title('Motor Tenp Vs. Time')
plt.ylabel('Temp (Celsius)')
plt.xlabel('Time(s)')
plt.savefig(currdir + test + 'figure1.png')
# plt.show()
# analyze but don't convert
compData = arrange(data, brakeStrength, currdir, test, convert=False)
avgCurrent = compData[:, -1]
# load lake Placid data
placidData = np.genfromtxt('data/ActualPlacidData.csv', delimiter=',')
placidData[0, 0] = 1.13
placidData = np.tile(placidData, (runs, 1))
# do square root fit
p, cov = curve_fit(
itoT, avgCurrent, placidData[:, 0],
[-121, 0.0111 * np.sqrt(40160000), 900000000 / 40160000])
# print parameters
print(p)