def PlotWindSpeed(self, e, c, s, params):
if s is not None:
wind_g = s['wind_sensor']['wind_g']
plot(s['time'], numpy_utils.Vec3Norm(wind_g), 'C1--',
label='wind speed at wind sensor [sim]')
wind_g = s['wing']['wind_g']
plot(s['time'], numpy_utils.Vec3Norm(wind_g), 'C2:',
label='wind speed at kite [sim]')
# Plot the estimated wind speed last so that it will be on top.
plot(c['time'], c['state_est']['wind_g']['speed_f'], 'C0-',
linewidth=2, label='wind speed [est]')
def PlotMagnetometer(self, g, s, params):
mplot.PlotVec3(g['estimate']['time'],
g['input']['imu']['mag'],
label='mag')
plot(g['estimate']['time'],
numpy_utils.Vec3Norm(g['input']['imu']['mag']),
'k--',
label='mag norm')
def CheckOverTension(monitor_params, sim_telem):
"""Tests that the tether tension stays below upper limits."""
max_tension = monitor_params['tether']['tension']['very_high']
tether_force = sim_telem['tether']['end_force_g']
log_max_tension = numpy.max(numpy_utils.Vec3Norm(tether_force))
print ('Max tension in flight was %3.1f kN (limit %3.1f kN).'
% (log_max_tension / 1000.0, max_tension / 1000.0))
if log_max_tension > max_tension:
raise FlightError('Over tension (%3.1f > %3.1f [N]).' %
(log_max_tension / 1000.0, max_tension / 1000.0))
def testH5MathHelpers(self):
num_samples = 10
# A fake log file is used strictly to get appropriate dtypes for the Vec3
# and Mat3 timeseries.
with tempfile.NamedTemporaryFile(suffix='.h5', delete=False) as tmp:
file_name = tmp.name
log_file = test_util.CreateSampleHDF5File(file_name, 1)
state_est = (log_file['messages']['kAioNodeControllerA']
['kMessageTypeControlTelemetry']['message']['state_est'])
vec3 = numpy.zeros(num_samples, dtype=state_est['Xg'].dtype)
mat3 = numpy.zeros(num_samples, dtype=state_est['dcm_g2b'].dtype)
log_file.close()
os.remove(file_name)
# Populate vec3 and mat3 with random data.
for axis in ('x', 'y', 'z'):
vec3[axis] = numpy.random.rand(num_samples)
mat3['d'] = numpy.random.rand(num_samples, 3, 3)
# Test Vec3ToArray. Values of `vec3` and `array` should be bitwise
# identical, so equality is appropriate.
array = numpy_utils.Vec3ToArray(vec3)
for i, axis in enumerate(('x', 'y', 'z')):
self.assertTrue((array[:, i] == vec3[axis]).all())
# Test Vec3Norm.
norm1 = (vec3['x']**2.0 + vec3['y']**2.0 + vec3['z']**2.0)**0.5
norm2 = numpy_utils.Vec3Norm(vec3)
self.assertTrue((numpy.abs(norm1 - norm2) < 1e-15).all())
# Test Mat3Vec3Mult.
mult = numpy_utils.Mat3Vec3Mult(mat3, vec3)
for i in range(num_samples):
b_expected = numpy.dot(
mat3['d'][i],
numpy.array([vec3['x'][i], vec3['y'][i], vec3['z'][i]]))
self.assertTrue((numpy.abs(mult[i, :] - b_expected) < 1e-15).all())
# Test Mat3TransVec3Mult.
trans_mult = numpy_utils.Mat3TransVec3Mult(mat3, vec3)
for i in range(num_samples):
b_expected = numpy.dot(
mat3['d'][i].T,
numpy.array([vec3['x'][i], vec3['y'][i], vec3['z'][i]]))
self.assertTrue((numpy.abs(trans_mult[i, :] - b_expected) < 1e-15).all())
def PlotTurbulenceIntensity(self, c, window_minutes=5):
assert isinstance(window_minutes, int)
assert window_minutes >= 1
df = pd.DataFrame(index=pd.to_timedelta(c['time'], unit='s'))
df['wind_g_mag'] = numpy_utils.Vec3Norm(
c['state_est']['wind_g']['vector'])
df_1s = df.resample('1s', label='right').agg(['mean', 'std'])
df_1s_nt = df_1s.xs('mean', level=1, axis='columns').rolling(
window='%dT' % window_minutes, min_periods=window_minutes *
60).agg(['mean', 'std']).shift(periods=(-window_minutes * 60) // 2,
freq='s')
df_1s_nt_ti = (df_1s_nt[('wind_g_mag', 'std')] /
df_1s_nt[('wind_g_mag', 'mean')])
ylabel('TI (over %d-minute rolling calculations)' % window_minutes)
plot(df_1s_nt_ti.keys().total_seconds(), df_1s_nt_ti)
def PlotWindSpeedAloft(self, c):
wind_aloft_g_mag = numpy_utils.Vec3Norm(
c['state_est']['wind_aloft_g']['vector'])
plot(c['time'], wind_aloft_g_mag)