def __init__(self, filename):
self.messages = load_messages(filename)
# messages with gitsha1 are transmitted before the simulation loop
for i, msg in enumerate(self.messages):
if msg.HasField('gitsha1'):
start_index = i + 1
else:
break
self.records = get_records_from_messages(self.messages)[start_index:]
self.t = get_time_vector(self.records)
self.states = self.records.state[:, 1:] # skip yaw angle
self.colors = np.roll(sns.color_palette('Paired', 12), 2, axis=0)
steer_encoder_counts_per_rev = 152000
encoder_count = self.records.sensors.steer_encoder_count
encoder_angle = encoder_count / steer_encoder_counts_per_rev * 2*np.pi
encoder_angle[np.where(encoder_angle > np.pi)[0]] -= 2*np.pi
self.measured_steer_angle = encoder_angle
self.kollmorgen_command_torque = convert_kollmorgen_command_dac(
self.records.actuators.kollmorgen_command_torque)
self.kollmorgen_applied_torque = convert_kollmorgen_applied_adc(
self.records.sensors.kollmorgen_actual_torque)
self.kistler_sensor_torque = convert_kistler_torque_adc(
self.records.sensors.kistler_measured_torque)
def __init__(self, filename):
self.messages = load_messages(filename)
# messages with gitsha1 are transmitted before the simulation loop
for i, msg in enumerate(self.messages):
if msg.HasField('gitsha1'):
start_index = i + 1
else:
break
self.records = get_records_from_messages(self.messages)[start_index:]
self.t = get_time_vector(self.records)
self.states = self.records.state[:, 1:] # skip yaw angle
self.colors = np.roll(sns.color_palette('Paired', 12), 2, axis=0)
steer_encoder_counts_per_rev = 152000
encoder_count = self.records.sensors.steer_encoder_count
encoder_angle = encoder_count / steer_encoder_counts_per_rev * 2 * np.pi
encoder_angle[np.where(encoder_angle > np.pi)[0]] -= 2 * np.pi
self.measured_steer_angle = encoder_angle
self.kollmorgen_command_torque = convert_kollmorgen_command_dac(
self.records.actuators.kollmorgen_command_torque)
self.kollmorgen_applied_torque = convert_kollmorgen_applied_adc(
self.records.sensors.kollmorgen_actual_torque)
self.kistler_sensor_torque = convert_kistler_torque_adc(
self.records.sensors.kistler_measured_torque)
return x
if __name__ == '__main__':
import sys
import matplotlib.pyplot as plt
from plot_sim import plot_states
sim_time = 3 # time for our Whipple simulation or upper limit [s]
start_index = 50 # number of records to skip to allow some time for state
# estimate to converge
if len(sys.argv) > 1:
from load_sim import (load_messages, get_records_from_messages,
get_time_vector)
messages = load_messages(sys.argv[1])
records = get_records_from_messages(messages)
t = get_time_vector(records[start_index:])
t -= t[0] # redefine zero time for simulator data
m = messages[0]
v = m.model.v
dt = m.model.dt
if t[-1] < sim_time:
sim_time = t[-1]
n = int(sim_time / dt)
states = records[start_index:].state[:, 1:]
fig2, ax2 = plot_states(t[:n], states[:n, :], to_degrees=True)
fig2.suptitle('Phobos simulator')
x0 = np.array(messages[start_index].state.x[1:]).reshape((4, 1))
import sys
import numpy as np
import matplotlib.pyplot as plt
from matplotlib2tikz import save as tikz_save
import seaborn as sns
sns.set_style('whitegrid')
sns.set_context('paper')
file_dir = os.path.dirname(os.path.realpath(__file__))
sys.path.append(os.path.abspath(os.path.join(file_dir, os.pardir)))
from plot_sim import plot_states
from load_sim import load_messages, get_records_from_messages, get_time_vector
from simulate_whipple_benchmark import simulate
if __name__ == '__main__':
messages = load_messages(os.path.join(file_dir, 'whipple.pb'))
start_index = 50 # ignore some samples to give Kalman state estimate
# time to converge
sim_time = 3
stride = 10 # don't plot all the points
records = get_records_from_messages(messages)[start_index::stride]
t = get_time_vector(records)
t -= t[0] # redefine zero time for simulator data
# plot in degrees because understanding radians is harder
states = records.state[:, 1:]
fig1, ax1 = plot_states(t, states, second_yaxis=True, to_degrees=True)
m = messages[0]
v = m.model.v
import numpy as np
import matplotlib.pyplot as plt
#from matplotlib2tikz import save as tikz_save
import seaborn as sns
sns.set_style('whitegrid')
sns.set_context('paper')
file_dir = os.path.dirname(os.path.realpath(__file__))
sys.path.append(os.path.abspath(os.path.join(file_dir, os.pardir)))
from plot_sim import plot_states
from load_sim import load_messages, get_records_from_messages, get_time_vector
from simulate_whipple_benchmark import simulate, calculate_weave_frequency
if __name__ == '__main__':
if len(sys.argv) > 1:
messages = load_messages(sys.argv[1])
else:
messages = load_messages(os.path.join(file_dir, 'whipple.pb'))
# ignore first sample as it is transmitted before the simulation loop
records = get_records_from_messages(messages[1:])
if len(sys.argv) >= 5:
start = float(sys.argv[2])
stop = float(sys.argv[3])
stride = int(sys.argv[4])
t = get_time_vector(records)
t -= t[0]
start_index = np.squeeze(np.argwhere(t >= start))[0]
sim_time = stop - start
return x
if __name__ == '__main__':
import sys
import matplotlib.pyplot as plt
from plot_sim import plot_states
sim_time = 3 # time for our Whipple simulation or upper limit [s]
start_index = 50 # number of records to skip to allow some time for state
# estimate to converge
if len(sys.argv) > 1:
from load_sim import (load_messages, get_records_from_messages,
get_time_vector)
messages = load_messages(sys.argv[1])
records = get_records_from_messages(messages)
t = get_time_vector(records[start_index:])
t -= t[0] # redefine zero time for simulator data
m = messages[0]
v = m.model.v
dt = m.model.dt
if t[-1] < sim_time:
sim_time = t[-1]
n = int(sim_time/dt)
states = records[start_index:].state[:, 1:]
fig2, ax2 = plot_states(t[:n], states[:n, :], to_degrees=True)
fig2.suptitle('Phobos simulator')
x0 = np.array(messages[start_index].state.x[1:]).reshape((4, 1))
import sys
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import seaborn as sns
sns.set_style('whitegrid', {'legend.frameon': True})
sns.set_context('talk')
file_dir = os.path.dirname(os.path.realpath(__file__))
sys.path.append(os.path.abspath(os.path.join(file_dir, os.pardir)))
from load_sim import load_messages, get_records_from_messages, get_time_vector
from simulate_whipple_benchmark import simulate, calculate_weave_frequency
if __name__ == '__main__':
filename = sys.argv[1]
messages = load_messages(filename)
# ignore first sample as it is transmitted before the simulation loop
records = get_records_from_messages(messages[1:])
t = get_time_vector(records)
t -= t[0]
state = records.state[:, 1:] # skip yaw angle
colors = np.roll(sns.color_palette('Paired', 10), 2, axis=0)
labels = [
r'$\phi$, roll angle',
r'$\delta$, steer angle',
r'$\dot{\phi}$, roll rate',
r'$\dot{\delta}$, steer rate',
]