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 simAngle(eng, mass, TT, error_rate, error_time, errors):
if error_rate > 0:
Sandbox.analyze_results(1, 1, *[simTurnTime(0, mass, eng, tt, error_rate, error_time) for tt in TT])
else:
cols = len(errors)
for c, error in enumerate(errors):
Sandbox.analyze_results(cols, c + 1, *[simTurnTime(error, mass, eng, tt) for tt in TT])
fig = plt.gcf()
fig.canvas.set_window_title(datetime.strftime(datetime.now(), '%H:%M:%S'))
plt_show_maxed()
def simAngle(eng, AA, base_thrust, wheels_ratio, error_rate, error_time, angles):
if error_rate > 0:
ATC.analyze_results(1, 1, *[simAA(0, aa, eng, base_thrust, wheels_ratio, error_rate, error_time) for aa in AA])
else:
cols = len(angles)
for c, ang in enumerate(angles):
ATC.analyze_results(cols, c + 1, *[simAA(ang, aa, eng, base_thrust, wheels_ratio) for aa in AA])
fig = plt.gcf()
fig.canvas.set_window_title(datetime.strftime(datetime.now(), '%H:%M:%S'))
plt_show_maxed()
def simAngle(AAs, error_rate, error_time, angles):
if error_rate > 0:
BRC.analyze_results(1, 1,
*[simAA(0, aa, error_rate, error_time) for aa in AAs])
else:
cols = len(angles)
for c, ang in enumerate(angles):
BRC.analyze_results(cols, c + 1, *[simAA(ang, aa) for aa in AAs])
fig = plt.gcf()
fig.canvas.set_window_title(datetime.strftime(datetime.now(), '%H:%M:%S'))
plt_show_maxed()
def timelines(t, *lines):
n = len(lines)
ax1 = None
for i, line in enumerate(lines):
ax = plt.subplot(n, 1, i + 1, sharex=ax1)
plt.plot(t, line)
plt.grid(b=False, which='major', axis='x', color='g', linestyle='-')
plt.grid(b=False, which='minor', axis='x', color='0.15', linestyle='--')
plt.grid(b=False, which='major', axis='y', color='g', linestyle='-')
if ax1 is None:
ax1 = ax
plt_show_maxed()
def simAngle(AAs, error_rate, error_time, angles):
if error_rate > 0:
BRC.analyze_results(
1, 1, *[simAA(0, aa, error_rate, error_time) for aa in AAs])
else:
cols = len(angles)
for c, ang in enumerate(angles):
BRC.analyze_results(cols, c + 1,
*[simAA(ang, aa) for aa in AAs])
fig = plt.gcf()
fig.canvas.set_window_title(
datetime.strftime(datetime.now(), '%H:%M:%S'))
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 simAngle(eng, mass, TT, error_rate, error_time, errors):
if error_rate > 0:
Sandbox.analyze_results(
1, 1, *[
simTurnTime(0, mass, eng, tt, error_rate, error_time)
for tt in TT
])
else:
cols = len(errors)
for c, error in enumerate(errors):
Sandbox.analyze_results(
cols, c + 1,
*[simTurnTime(error, mass, eng, tt) for tt in TT])
fig = plt.gcf()
fig.canvas.set_window_title(
datetime.strftime(datetime.now(), '%H:%M:%S'))
plt_show_maxed()
def simAngle(eng, AA, base_thrust, wheels_ratio, error_rate, angles):
cols = 1 if error_rate > 0 else len(angles)
if error_rate > 0:
ATC.analyze_results(
cols, 1, *[
simAA(0, aa, eng, base_thrust, wheels_ratio, error_rate)
for aa in AA
])
else:
for c, ang in enumerate(angles):
ATC.analyze_results(
cols, c + 1, *[
simAA(ang, aa, eng, base_thrust, wheels_ratio)
for aa in AA
])
fig = plt.gcf()
fig.canvas.set_window_title(
datetime.strftime(datetime.now(), '%H:%M:%S'))
plt_show_maxed()
def sim_Altitude():
# vs = loadCSV('VS-filtering-26.csv', ('AbsAlt', 'TerAlt', 'Alt', 'AltAhead', 'Err', 'VSP', 'aV', 'rV', 'dV'))
# # ter = vs.TerAlt[-10:10:-1]
# vs = vs[vs.Alt > 3]
# ter = vs.TerAlt[17000:22000]
sim1 = VSF_sim(AS=0.12, DS=0.5)
sim1.t1 = 60.0
thrust = [100] # np.arange(100, 300, 50)
def run_sim(c, n, A, sA, pid):
for t in thrust:
sim1.run_alt(A, sA, thrust=t, pid=pid)
i = 0;
r = 7
sim1.plot_alt(r, c, c * i + n)
i += 1
sim1.plot_ter(r, c, c * i + n)
i += 1
sim1.plot_ralt(r, c, c * i + n)
i += 1
sim1.plot_vs(r, c, c * i + n)
i += 1
sim1.plot_vsp(r, c, c * i + n)
i += 1
sim1.plot_dvsp(r, c, c * i + n)
i += 1
sim1.plot_k(r, c, c * i + n)
i += 1
sim1.describe()
# run_sim(2,1, 100.0, pid)
run_sim(2, 1, 50.0, 0.0, PID2(0.3, 0.0, 0.3, -9.9, 9.9))
run_sim(2, 2, 50.0, 105.0, PID2(0.3, 0.0, 0.3, -9.9, 9.9))
# run_sim(4,3, -alt/10.0, pid)
# run_sim(4,4, -alt, pid)
sim1.legend()
plt_show_maxed()
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 timelines(t, *lines):
n = len(lines)
ax1 = None
for i, line in enumerate(lines):
ax = plt.subplot(n, 1, i + 1, sharex=ax1)
plt.plot(t, line)
plt.grid(b=False,
which='major',
axis='x',
color='g',
linestyle='-')
plt.grid(b=False,
which='minor',
axis='x',
color='0.15',
linestyle='--')
plt.grid(b=False,
which='major',
axis='y',
color='g',
linestyle='-')
if ax1 is None:
ax1 = ax
plt_show_maxed()
vy = [start_vel * np.sin(start_angle)]
a = [start_angle]
t = [0]
m = self.M
while a[-1] > end_angle:
accel = self.T / m
thrust_angle = a[-1] - self.a
ax = accel * np.cos(thrust_angle)
ay = accel * np.sin(thrust_angle) - self.G
m -= self.mflow * dt
vx.append(vx[-1] + ax * dt)
vy.append(vy[-1] + ay * dt)
x.append(x[-1] + vx[-1] * dt)
y.append(y[-1] + vy[-1] * dt)
a.append(np.arctan2(vy[-1], vx[-1]))
t.append(t[-1] + dt)
print(t[-1], vx[-1], vy[-1],
np.sqrt(vx[-1] * vx[-1] + vy[-1] * vy[-1]))
return t, a, x, y, vx, vy
if __name__ == '__main__':
import matplotlib.pyplot as plt
sim = GTurn(2, 15, 0.01, 5)
t, a, x, y, vx, vy = sim.simulate(50, 90, 30)
a = np.rad2deg(a)
plt.plot(t, a)
plt.quiver(t, a, vx, vy, units='xy', width=0.02, angles='xy')
plt_show_maxed()
vx = [start_vel * np.cos(start_angle)]
vy = [start_vel * np.sin(start_angle)]
a = [start_angle]
t = [0]
m = self.M
while a[-1] > end_angle:
accel = self.T / m
thrust_angle = a[-1] - self.a
ax = accel * np.cos(thrust_angle)
ay = accel * np.sin(thrust_angle) - self.G
m -= self.mflow * dt
vx.append(vx[-1] + ax * dt)
vy.append(vy[-1] + ay * dt)
x.append(x[-1] + vx[-1] * dt)
y.append(y[-1] + vy[-1] * dt)
a.append(np.arctan2(vy[-1], vx[-1]))
t.append(t[-1] + dt)
print(t[-1], vx[-1], vy[-1], np.sqrt(vx[-1] * vx[-1] + vy[-1] * vy[-1]))
return t, a, x, y, vx, vy
if __name__ == '__main__':
import matplotlib.pyplot as plt
sim = GTurn(2, 15, 0.01, 5)
t, a, x, y, vx, vy = sim.simulate(50, 90, 30)
a = np.rad2deg(a)
plt.plot(t, a)
plt.quiver(t, a, vx, vy, units='xy', width=0.02, angles='xy')
plt_show_maxed()
def boxplot(df, columns=None):
cnames = df.keys() if columns is None else columns
plt.boxplot([df[k] for k in cnames], labels=cnames)
plt_show_maxed()
def boxplot(df, columns=None):
cnames = df.keys() if columns is None else columns
plt.boxplot([df[k] for k in cnames], labels=cnames)
plt_show_maxed()