def test_with_plain_values(self):
bidfm = BallInfoDataFilterModule()
t = time.time()
yu = [1300, 1200, 1100, 1000, 900]
yv = [0, 10, 30, 45, 65]
timestamps = [t, t+0.06, t+0.12, t+0.18, t+0.24]
print "Result", bidfm.calculate_regression(yu, yv, timestamps, look_into_future=0.78)
def test_uv_prediction(self):
data = {}
ballMock = PyMock()
data["BallInfo"] = ballMock
data["BallFound"] = True
data[DATA_KEY_IS_NEW_FRAME] = True
us = [(i/20.0)**3 for i in range(50)]
vs = [(i/20.0)**2 for i in range(50)]
time = range(50)
us = us[0:30] + us[30:50]
vs = vs[0:30] + vs[30:50]
time = time[0:30] + time[30:50]
us[30] = 10
bidfm = BallInfoDataFilterModule()
bidfm.start(data)
predictions = []
grades = []
for i in range(len(us)):
data["BallInfo"]._setSomething("u", us[i])
data["BallInfo"]._setSomething("v", vs[i])
data[DATA_KEY_CAMERA_CAPTURE_TIMESTAMP] = time[i]
# Simplistic counter for increasing time
# Call the module
bidfm.update(data)
self.assertTrue("uvprediction" in data[DATA_KEY_BALL_INFO_FILTERED])
predictions.append(data[DATA_KEY_BALL_INFO_FILTERED]["uvprediction"])
grades.append(data[DATA_KEY_BALL_INFO_FILTERED]["uvgrade"])
if False:
import matplotlib.pyplot as plt
self.draw_first(predictions, us, vs, 1, 0, 50)
# Draw the measured u value for this point in time
#plt.plot(range(len(grades)), [math.sqrt(e[0]**2+e[1]**2) for e in grades], 'r-')
# COMMENT - The red dots are the real u values measured,
# the blue values the estimated values in 5 time steps
# The green dotted lines are for better reading the u value
# the Vertical lines describe when the estimation of u in 5 time steps is below zero
plt.show()