irlmodelvalidation/compute_data_statistics.py at master · tud-hri/irlmodelvalidation · GitHub

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"""
Copyright 2021, Olger Siebinga (o.siebinga@tudelft.nl)

This file is part of the module irlmodelvalidation.

irlmodelvalidation is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

irlmodelvalidation is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with irlmodelvalidation.  If not, see <https://www.gnu.org/licenses/>.
"""
import tqdm

from processing.encryptiontools import load_encrypted_pickle
from dataobjects import HighDDataset

if __name__ == '__main__':
    data: HighDDataset

    max_y_acc = 0.0
    max_x_acc = 0.0
    min_x_acc = 0.0

    total_vehicles = 0
    total_time = 0.
    road_length = []
    total_mean_v = 0.

    for index in tqdm.trange(1, 61):
        data = load_encrypted_pickle('../data/HighD/data/%02d.pkl' % index)

        max_y_acc_in_data = max(data.track_data['yAcceleration'].max(), -data.track_data['yAcceleration'].min())
        max_x_acc_in_data = data.track_data['xAcceleration'].max()
        min_x_acc_in_data = data.track_data['xAcceleration'].min()

        total_time += data.total_driven_time
        total_vehicles += data.num_vehicles
        road_length.append(float(data.track_data['x'].max()))

        total_mean_v += data.track_meta_data['meanXVelocity'].sum()

        if max_y_acc_in_data > max_y_acc:
            max_y_acc = max_y_acc_in_data
        if max_x_acc_in_data > max_x_acc:
            max_x_acc = max_x_acc_in_data
        if min_x_acc_in_data < min_x_acc:
            min_x_acc = min_x_acc_in_data

    print('max y = ', max_y_acc)
    print('max x = ', max_x_acc)
    print('min x = ', min_x_acc)

    print('average duration of a track = %.2f' % (total_time/total_vehicles))
    print('mean vehicle velocity = %.2f' % (total_mean_v / total_vehicles))
    print('average road length per recording = %.1f' % (sum(road_length) / len(road_length)))