def main():
print("Main Start")
g = gr.Graph()
g.readVertexFile('vertices.txt')
g.readEdgeFile('only_with_pairs.txt')
print(len(g.V))
drawing.setID(g) # вставь граф
physics.setConstans(g)
while True:
physics.physStep(g)
drawing.drawing(g) #вставь граф
drawing.canvas.update()
rotate.rotate(g, [1, 2, 3], 0.01)
def main():
# default simulation parameter values
g = 10
dt = 0.04
m1 = float(input("Mass 1 : "))
m2 = 7.0
t1 = t2 = 0.5
w1 = w2 = 2.5
L1 = L2 = 1.0
# default window dimensions
cwidth = cheight = 500
# by default, use the Euler-Lagrange equations to simulate the system
lagrangian = True
# in test mode, no window is displayed (only text output is produced)
test_mode = False
# initialize the double pendulum
if lagrangian == True:
from dp_lagrangian import DoublePendulumLagrangian
obj = DoublePendulumLagrangian(g, m1, m2, t1, t2, w1, w2, L1, L2)
else:
from dp_hamiltonian import DoublePendulumHamiltonian
obj = DoublePendulumHamiltonian(g, m1, m2, t1, t2, w1, w2, L1, L2)
step = 0
# maximum energy change (compared to E0): too large => unstable simulation
# set up the clock and the output window
if test_mode == False:
pygame.init()
clock = pygame.time.Clock()
canvas = pygame.display.set_mode((cwidth, cheight), pygame.RESIZABLE)
pygame.display.set_caption("double pendulum")
# keep running the simulation until the user closes the window
while True:
# redraw the double pendulum at a maximum rate of 25 fps
if test_mode == False:
drawing.drawing(obj, canvas, cwidth, cheight, dt)
clock.tick(25)
# advance one time step
obj.time_step(dt)
step += 1
def main2():
print("Main Start")
g1 = gr.Graph()
g2 = gr.Graph()
g1.createCircle(2)
g2.createCircle(2)
g3 = gr.multiply(g1, g2)
g4 = gr.multiply(g1, g3)
g = gr.multiply(g1, g3)
print(len(g.V))
drawing.setID(g) # вставь граф
physics.setConstans(g)
while True:
physics.physStep(g)
physics.physStep(g)
drawing.drawing(g) #вставь граф
drawing.canvas.update()
rotate.rotate(g, [1, 2, 3], 0.001)
time.sleep(1/60)
def printMap():
draw = drawing.drawing()
draw.setAxes(x_min=-1000, x_max=13000, y_min=-1000, y_max=18000)
draw = drawObstacle(draw)
map_data = readJsonFile(path + filename)
for key in map_data.keys():
is_enable = map_data[key]['enable']
x_cood = map_data[key]['cood'][0]
y_cood = map_data[key]['cood'][1]
if is_enable == True:
draw.drawCircle(x_cood, y_cood, 50, "#A4C520", True)
if is_enable == False:
draw.drawCircle(x_cood, y_cood, 50, "#717171", True)
draw.show()
def main():
er_sum = 0
num = 0
#基地局からの距離
dis_deviceA = 0
dis_deviceB = 0
dis_deviceC = 0
c = np.mat([[1, 0], [0, 1]])
p_1 = np.mat([[AP_A_X], [AP_A_Y]])
p_2 = np.mat([[AP_B_X], [AP_B_Y]])
p_3 = np.mat([[AP_C_X], [AP_C_Y]])
draw = drawing.drawing()
draw = setDraw(draw)
'''
# 歩いていると仮定した場合
for i in range(POINT_NUM):
truePosition = selectPosition(i + 1)
draw.drawCircle(truePosition[0][0], truePosition[1][0], 100, "#050A11", True)'''
if filter == 1:
kf = kalman.KalmanFilter(TRUE_POSITION_1_X, TRUE_POSITION_1_Y, p_1, p_2, p_3)
#kf = kalman.KalmanFilter(0, 0, p_1, p_2, p_3)
kf.setModel(o_walk=O_WALK, o_range=O_RANGE)
#kf.setModel(O_WALK, O_RANGE, r)
elif filter == 2:
bf = bayesian.BaysianFilter(POINT_1_GRID, p_i, sigma_range)
bf.readMap(map_path)
elif filter == 3:
bkm = bkmixture.BKmixture(p_i)
bkm.setKalman([TRUE_POSITION_1_X, TRUE_POSITION_1_Y], p_1, p_2, p_3, O_WALK, O_RANGE, r)
bkm.setBayesian(sigma_range, map_path)
with open(write_path + write_file + '8' + '.csv', mode='w') as fw:
with open(read_path + read_file + 'A' + '.csv', encoding='utf-8', mode='r') as f:
for line in f:
data = line.split(',')
distanceMm = int(data[0])
getID = int(data[4])
#truenum = int(data[5])
#ファイルから取得したデータをcalibrate
if getID == DEVICE_A_ID:
dis_deviceA = calibration(getID, distanceMm)
elif getID == DEVICE_B_ID:
dis_deviceB = calibration(getID, distanceMm)
elif getID == DEVICE_C_ID:
dis_deviceC = calibration(getID, distanceMm)
#測距結果を用いて測位
if dis_deviceA != 0 and dis_deviceB != 0 and dis_deviceC != 0:
Z = np.mat([[dis_deviceA], [dis_deviceB], [dis_deviceC]])
time = 0
if filter == 1:
kf.estimate()
#kf.filter(Z)
kf.filter(np.square(Z))
x = kf.getStatus()
elif filter == 2:
grid = 1
was_grid = 0
for key in range(8):
was_grid = grid
bf.prediction()
x, grid = bf.update(Z)
if grid != was_grid:
time += 1
print(str(grid) + ',' + str(was_grid))
elif filter == 3:
x, grid = bkm.estimate(Z)
dis_deviceA = 0
dis_deviceB = 0
dis_deviceC = 0
#er = error(x, num + 1)
#er_sum += er
fw.write(str(int(x[0, 0])) + ',' + str(int(x[1, 0])) + ',' + str(time) + ',' + '\n')
mod = num % 8
if mod == 0:
draw.drawCircle(x[0, 0], x[1, 0], 100, "#EDAD0B", True)
elif mod == 1:
draw.drawCircle(x[0, 0], x[1, 0], 100, "#C7243A", True)
elif mod == 2:
draw.drawCircle(x[0, 0], x[1, 0], 100, "#D8E212", True)
elif mod == 3:
draw.drawCircle(x[0, 0], x[1, 0], 100, "#23AC0E", True)
elif mod == 4:
draw.drawCircle(x[0, 0], x[1, 0], 100, "#0086AB", True)
elif mod == 5:
draw.drawCircle(x[0, 0], x[1, 0], 100, "#3261AB", True)
elif mod == 6:
draw.drawCircle(x[0, 0], x[1, 0], 100, "#5D639E", True)
elif mod == 7:
draw.drawCircle(x[0, 0], x[1, 0], 100, "#A52175", True)
num += 1
#print('a')
#draw.pause(0.1)
draw.show()
'''
for num in range(POINT_NUM):
filename = FILE_NAME
truePosition = selectPosition(num + 1)
print("now:" + str(now_grid))
print("position:" + str(truePosition))
draw.drawCircle(truePosition[0, 0], truePosition[1, 0], 100, "#050A11", True)
if filter == 1:
#kf = kalman.KalmanFilter(truePosition[0, 0], truePosition[1, 0], p_1, p_2, p_3)
kf = kalman.KalmanFilter(0, 0, p_1, p_2, p_3)
kf.setModel(O_WALK, O_RANGE, r)
elif filter == 2:
bf = bayesian.BaysianFilter(now_grid, p_i, sigma_range)
# bf.readMap('/Users/andy/Wifi_rtt/mapInfo.json')
bf.readMap(map_path)
elif filter == 3:
bkm = bkmixture.BKmixture(p_i)
bkm.setKalman([truePosition[0, 0], truePosition[1, 0]], p_1, p_2, p_3, O_WALK, O_RANGE, r)
bkm.setBayesian(sigma_range, map_path)
with open(write_path + write_file + str(num+1) + '.csv', mode='w') as fw:
with open(read_path + read_file + str(num+1) + '.csv', encoding='utf-8', mode='r') as f:
f.readline()
for line in f:
data = line.split(',')
distanceMm = int(data[0])
getID = int(data[4])
#truenum = int(data[5])
#ファイルから取得したデータをcalibrate
if getID == DEVICE_A_ID:
dis_deviceA = calibration(getID, distanceMm)
elif getID == DEVICE_B_ID:
dis_deviceB = calibration(getID, distanceMm)
elif getID == DEVICE_C_ID:
dis_deviceC = calibration(getID, distanceMm)
#測距結果を用いて測位
if dis_deviceA != 0 and dis_deviceB != 0 and dis_deviceC != 0:
Z = np.mat([[dis_deviceA], [dis_deviceB], [dis_deviceC]])
if filter == 1:
kf.estimate()
#kf.filter(Z)
kf.filter(np.square(Z))
x = kf.getStatus()
elif filter == 2:
grid = 1
was_grid = 0
for key in range(5):
was_grid = grid
bf.prediction()
x, grid = bf.update(Z)
print(str(grid) + ',' + str(was_grid))
elif filter == 3:
x, grid = bkm.estimate(Z)
dis_deviceA = 0
dis_deviceB = 0
dis_deviceC = 0
er = error(x, num + 1)
er_sum += er
fw.write(str(x[0, 0]) + ',' + str(x[1, 0]) + ',' + str(er) + ',' + '\n')
draw.drawCircle(x[0, 0], x[1, 0], 100, "#EDAD0B", True)
#print('a')
#draw.pause(0.1)
'''
#draw.show()
return
# coding=utf-8
import argparse
from drawing import drawing
parser = argparse.ArgumentParser(description=u"Aplica efeito de desenho a lápis em foto.")
group = parser.add_mutually_exclusive_group()
group.add_argument("-v", "--verbose", action="store_true")
group.add_argument("-q", "--quiet", action="store_true")
parser.add_argument("foto", type=str, help=u"Nome da foto que será transformada. Ex.: foto.jpg")
parser.add_argument("desenho", type=str, help=u"Nome da foto depois de transformada. Ex.: desenho.jpg")
parser.add_argument("--blur", type=int, default=25, dest="blur", help=u"Configura o valor da desfocagem. Ex.: 25")
parser.add_argument("--alpha", type=float, default=1.0, dest="alpha", help=u"Configura o valor da transparencia. Ex.: 0.5 para 50% de transparencia")
args = parser.parse_args()
drawing(args.foto, args.desenho, blur=args.blur, alpha=args.alpha)
if args.quiet:
pass
elif args.verbose:
print "A imagem {0} foi convertida para {1}".format(args.foto, args.desenho)
print "Com blur: {0} e alpha: {1}".format(args.blur, args.alpha)
else:
print "{0} -> {1}".format(args.foto, args.desenho)
result_value.append(temp_value)
true_value.append(math.sin(tt))
tt += 0.1
return result_value, true_value
t = [i for i in range(0, 11)]
y = []
true_value = []
for i in t:
noise = np.random.normal(loc=0, scale=0.2)
y.append(math.sin(i) + noise)
#used to dot the noise values
yy = [None for _ in range(101)]
for i in range(0, 11):
yy[i * 10] = y[i]
t = np.array(t)
y = np.array(y)
phi = np.vander(t)
x = np.dot(np.linalg.inv(phi), y.T)
result, true_value = calc(x)
print(y)
drawing(yy, result, true_value, '10dim')
def update_graph(checklist, range_l, n):
return draw.drawing(df, checklist, range_l)
import drawing drawing.drawing()