def drawDF(df, x, columns, colors=None, axes=None):
from collections import Counter
if axes is not None:
num_axes = Counter(axes)
nrows = max(num_axes.values())
ncols = len(num_axes.keys())
X = df[x]
ax1 = None
if colors is None: colors = color_grad(len(columns))
for i, k in enumerate(columns):
if axes is None:
plt.plot(X, df[k], label=k, color=colors[i])
else:
ax = plt.subplot(nrows,
ncols,
ncols * (i % nrows) + axes[i],
sharex=ax1)
ax.plot(X, df[k], label=k, color=colors[i])
plt.ylabel(k)
if ax1 is None: ax1 = ax
plt.minorticks_on()
plt.grid(b=False, which='major', axis='x', color='b', linestyle='-')
plt.grid(b=False,
which='minor',
axis='x',
color='0.15',
linestyle='--')
plt.grid(b=False, which='major', axis='y', color='b', linestyle='-')
# plt.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.0)
plt_show_maxed()
def sim_PointNav():
P = 2
D = 0.1
AAk = 0.5
pid = PID(P, 0, D, 0, 10)
accel = np.arange(0.1, 1.1, 0.1)
distance = 500
distanceF = 0.1
cols = color_grad(len(accel))
for i, a in enumerate(accel):
d = [distance]
v = [0]
act = [0]
t = 0
A = [0]
while d[-1] > 0:
pid.kp = P * a / clampL(v[-1], 0.01)
act.append(pid.update(d[-1] * distanceF))
aa = a * AAk
if v[-1] < pid.action:
if A[-1] < a: A.append(A[-1] + a * AAk * dt)
else: A.append(a)
elif v[-1] > pid.action:
if A[-1] > -a: A.append(A[-1] - a * AAk * dt)
else: A.append(-a)
elif A[-1] > 0: A.append(A[-1] - a * AAk * dt)
elif A[-1] < 0: A.append(A[-1] + a * AAk * dt)
else: A.append(0)
# print 'd=% 8.4f, v=% 8.4f, act=% 8.4f, A=% 8.4f' % (d[-1], v[-1], act[-1], A[-1])
v.append(v[-1] + A[-1] * dt)
d.append(d[-1] - v[-1] * dt)
t += dt
print 'accel=%.2f, time: %.1fs' % (a, t)
print len(d), len(A)
plt.subplot(3, 1, 1)
plt.plot(d, v, color=cols[i], label="accel=%.2f" % a)
plt.ylabel("V")
plt.xlabel("distance")
plt.subplot(3, 1, 2)
plt.plot(d, act, color=cols[i], label="accel=%.2f" % a)
plt.ylabel("act")
plt.xlabel("distance")
plt.subplot(3, 1, 3)
plt.plot(d, A, color=cols[i], label="accel=%.2f" % a)
plt.ylabel("real accel");
plt.xlabel("distance")
plt.legend()
plt_show_maxed()
def analyze_results(cls, cols, col, *results):
colors = color_grad(len(results))
for i, result in enumerate(results):
name, time, error, action, thrust, zero_stats = result
if zero_stats:
print '\n%s\n' % str(zero_stats)
print '=' * 80
ax = plt.subplot(3, cols, col)
plt.plot(time, error, label=name, color=colors[i])
plt.ylabel('error (deg)')
plt.legend()
plt.subplot(3, cols, cols+col, sharex=ax)
plt.plot(time, action, color=colors[i])
plt.ylabel('action')
plt.subplot(3, cols, cols*2+col, sharex=ax)
plt.plot(time, thrust, color=colors[i])
plt.xlabel('time')
plt.ylabel('thrust')
def analyze_results(cls, cols, col, *results):
colors = color_grad(len(results))
for i, result in enumerate(results):
name, time, error, action, thrust, zero_stats = result
if zero_stats:
print '\n%s\n' % str(zero_stats)
print '=' * 80
ax = plt.subplot(3, cols, col)
plt.plot(time, error, label=name, color=colors[i])
plt.ylabel('error (deg)')
plt.legend()
plt.subplot(3, cols, cols + col, sharex=ax)
plt.plot(time, action, color=colors[i])
plt.ylabel('action')
plt.subplot(3, cols, cols * 2 + col, sharex=ax)
plt.plot(time, thrust, color=colors[i])
plt.xlabel('time')
plt.ylabel('thrust')
def draw_vectors(*vecs):
if not vecs: return;
X, Y, Z, U, V, W = zip(*(xzy(v) + xzy(v) for v in vecs))
colors = color_grad(len(vecs), 3)
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.quiver(X, Y, Z, U, V, W, colors=colors, lw=2, arrow_length_ratio=0.1)
ax.set_xlim([-1, 1])
ax.set_ylim([-1, 1])
ax.set_zlim([-1, 1])
ax.set_xlabel('r')
ax.set_ylabel('f')
ax.set_zlabel('u')
legends = []
for i in range(len(vecs)):
legends.append(mlines.Line2D([], [], color=colors[i * 3], label=str(i + 1)))
plt.legend(handles=legends)
plt.show()
def drawDF(df, x, columns, colors=None, axes=None):
from collections import Counter
if axes is not None:
num_axes = Counter(axes)
nrows = max(num_axes.values())
ncols = len(num_axes.keys())
X = df[x]
ax1 = None
if colors is None: colors = color_grad(len(columns))
for i, k in enumerate(columns):
if axes is None:
plt.plot(X, df[k], label=k, color=colors[i])
else:
ax = plt.subplot(nrows, ncols, ncols * (i % nrows) + axes[i], sharex=ax1)
ax.plot(X, df[k], label=k, color=colors[i])
plt.ylabel(k)
if ax1 is None: ax1 = ax
plt.minorticks_on()
plt.grid(b=False, which='major', axis='x', color='b', linestyle='-')
plt.grid(b=False, which='minor', axis='x', color='0.15', linestyle='--')
plt.grid(b=False, which='major', axis='y', color='b', linestyle='-')
# plt.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.0)
plt_show_maxed()
def analyze_results(cls, cols, col, *results):
colors = color_grad(len(results) + 1)
gcolor = colors[-1]
for i, result in enumerate(results):
name, time, error, action, addons, zero_stats = result
if zero_stats:
print '\n%s\n' % str(zero_stats)
print '=' * 80
nplots = 2 + len(addons)
ax = plt.subplot(nplots, cols, col)
plt.plot(time, error, label=name, color=colors[i])
cls._draw_grid(gcolor)
plt.ylabel('error')
plt.legend()
plt.subplot(nplots, cols, cols + col, sharex=ax)
plt.plot(time, action, color=colors[i])
cls._draw_grid(gcolor)
plt.ylabel('action')
if addons:
for curve, ylab in addons:
plt.plot(time, curve, color=colors[i])
cls._draw_grid(gcolor)
plt.ylabel(ylab)
plt.xlabel('time')
def analyze_results(cls, cols, col, *results):
colors = color_grad(len(results)+1)
gcolor = colors[-1]
for i, result in enumerate(results):
name, time, error, action, addons, zero_stats = result
if zero_stats:
print('\n%s\n' % str(zero_stats))
print('=' * 80)
nplots = 2+len(addons)
ax = plt.subplot(nplots, cols, col)
plt.plot(time, error, label=name, color=colors[i])
cls._draw_grid(gcolor)
plt.ylabel('error')
plt.legend()
plt.subplot(nplots, cols, cols+col, sharex=ax)
plt.plot(time, action, color=colors[i])
cls._draw_grid(gcolor)
plt.ylabel('action')
if addons:
for curve, ylab in addons:
plt.plot(time, curve, color=colors[i])
cls._draw_grid(gcolor)
plt.ylabel(ylab)
plt.xlabel('time')