# create a second axis for onother data set
# to better visualize different scaled measures
fig1_ax2 = fig1_ax1.twinx()
fig1_ax2.set_ylabel("angular velocity (deg/s)", color='r')
fig1_ax2.tick_params("y", colors='r')
fig1_ax2.plot(times, -angular_velocities[2], color='r')
fig1_ax2.set_ylim(ylim)
fig1_ax2.set_xlim(xlim)
plt.legend()
if __name__ == "__main__":
window_size = 20
# currently default format is unmodified fullinertial but other formats are / will be supported
times, coordinates, altitudes, gps_speed, heading, accelerations, angular_velocities = parse_input(
sys.argv[1], [InputType.UNMOD_FULLINERTIAL])
converts_measurement_units(accelerations, np.array([0.1]), gps_speed,
coordinates, heading)
normalize_timestamp(times)
data_preprocessing_plot("Before")
# reduce accelerations disturbance
times, accelerations = reduce_disturbance(times, accelerations,
window_size)
# reduce angular velocities disturbance
_, angular_velocities = reduce_disturbance(times, angular_velocities,
window_size)
# slice gps_speed to remove null values
def get_trajectory_from_path(path):
"""
parse input file from path, clean data and integrate positions
:param path: string input file
:return: 3 numpy array: 3xn position, 1xn times, 4xn angular position as quaternions
"""
window_size = 20
# currently default format is unmodified fullinertial but other formats are / will be supported
times, coordinates, altitudes, gps_speed, heading, accelerations, angular_velocities = parse_input(
path, [InputType.UNMOD_FULLINERTIAL])
converts_measurement_units(accelerations, angular_velocities, gps_speed,
coordinates, heading)
# get positions from GNSS data
gnss_positions, headings_2 = get_positions(coordinates, altitudes)
# reduce accelerations disturbance
times, accelerations = reduce_disturbance(times, accelerations,
window_size)
# reduce angular velocities disturbance
_, angular_velocities = reduce_disturbance(times, angular_velocities,
window_size)
# truncate other array to match length of acc, thetas, times array
gnss_positions = gnss_positions[:,
round(window_size /
2):-round(window_size / 2)]
# with "final" times now get velocities and
real_velocities = get_velocities(times, gnss_positions)
# scalar speed from GNSS position (better than from dataset because avoids Kalmar filter)
real_speeds = np.linalg.norm(real_velocities, axis=0)
# get time windows where vehicle is stationary
stationary_times = get_stationary_times(gps_speed)
# clear gyroscope drift
angular_velocities = clear_gyro_drift(angular_velocities, stationary_times)
# set times start to 0
normalize_timestamp(times)
# correct z-axis alignment
accelerations, angular_velocities = correct_z_orientation(
accelerations, angular_velocities, stationary_times)
# remove g
accelerations[2] -= accelerations[
2, stationary_times[0][0]:stationary_times[0][-1]].mean()
# correct alignment in xy plane
#accelerations = correct_xy_orientation(accelerations, angular_velocities)
motion_time = get_first_motion_time(stationary_times, gnss_positions)
initial_angular_position = get_initial_angular_position(
gnss_positions, motion_time)
# convert to laboratory frame of reference
accelerations, angular_positions = rotate_accelerations(
times, accelerations, angular_velocities, heading,
initial_angular_position)
# rotate to align y to north, x to east
accelerations = align_to_world(gnss_positions, accelerations, motion_time)
# angular position doesn't need to be aligned to world if starting angular position is already aligned and following
# angular positions are calculated from that
initial_speed = np.array([[gps_speed[0]], [0], [0]])
# integrate acceleration with gss velocities correction
correct_velocities = cumulative_integrate(times,
accelerations,
initial_speed,
adjust_data=real_velocities,
adjust_frequency=1)
if sign_inversion_is_necessary(correct_velocities):
accelerations *= -1
correct_velocities *= -1
correct_position = cumulative_integrate(times,
correct_velocities,
adjust_data=gnss_positions,
adjust_frequency=1)
return correct_position, times, angular_positions
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
import numpy as np
import matplotlib.pyplot as plt
from src.input_manager import parse_input, InputType
from src.clean_data_utils import converts_measurement_units
from src.gnss_utils import get_positions
from plots_scripts.plot_utils import plot_vectors
if __name__ == "__main__":
# load dataset
crash_01_dataset = 'tests/test_fixtures/split_log_1321_1322_USB0_unmodified-fullinertial.txt'
times, coordinates, altitudes, gps_speed, heading, accelerations, angular_velocities = parse_input(
crash_01_dataset, [InputType.UNMOD_FULLINERTIAL])
converts_measurement_units(accelerations, angular_velocities, gps_speed,
coordinates)
#get cartesian position from world one
gnss_positions, headings = get_positions(coordinates, altitudes)
# creates empty array to fill
approx_points = np.zeros((2, len(times)))
# size of step using iterating array
step = round(len(times) / 10)
for i in range(0, len(times), step):
# creates local slice of times and positions
local_times = times[i:i + step]
local_positions = gnss_positions[0:2, i:i + step]
# calculate coefficients of polynomial approximation for x-coordinates
poly_x = np.polyfit(local_times, local_positions[0], deg=1)
# calculate coefficients of polynomial approximation for y-coordinates
from src.gnss_utils import get_positions, get_velocities, get_accelerations
from src.input_manager import parse_input, InputType
from src.integrate import cumulative_integrate
if __name__ == '__main__':
window_size = 20
# for benchmarking
import time
start_time = time.time()
#input path
parking_fullinertial_unmod = 'tests/test_fixtures/parking.tsv'
crash = False
times, coordinates, altitudes, gps_speed, heading, accelerations, angular_velocities = parse_input(
parking_fullinertial_unmod, [InputType.UNMOD_FULLINERTIAL])
converts_measurement_units(accelerations, angular_velocities, gps_speed, coordinates, heading)
period = times[1] - times[0]
times, gps_speed, accelerations, angular_velocities, coordinates, heading = \
truncate_if_crash(crash, times, gps_speed, accelerations, angular_velocities, coordinates, heading)
# get positions from GNSS data
gnss_positions, headings_2 = get_positions(coordinates, altitudes)
gnss_distance = np.linalg.norm(np.array([gnss_positions[:, i] - gnss_positions[:, i - 1]
for i, x in enumerate(gnss_positions[:, 1:].T, 1)]), axis=1).cumsum()
# insert initial distance
gnss_distance = np.insert(gnss_distance, 0, 0)
# reshape to 1xn shape
gnss_distance = np.reshape(gnss_distance, (1, len(gnss_distance)))
def setUp(self):
filepath = 'tests/test_fixtures/crash_01.txt'
self.times, self.gps_speed, self.accelerations, self.angular_velocities = parse_input(
filepath, [InputType.INERTIAL])
converts_measurement_units(self.accelerations, self.angular_velocities,
self.gps_speed)