PX4-Autopilot/Tools/ecl_ekf/analyse_logdata_ekf.py

#! /usr/bin/env python3
"""
the ecl ekf analysis
"""

from typing import Tuple, List, Dict

import numpy as np
from pyulog import ULog

from analysis.detectors import InAirDetector, PreconditionError
from analysis.metrics import calculate_ecl_ekf_metrics
from analysis.checks import perform_ecl_ekf_checks
from analysis.post_processing import get_gps_check_fail_flags

def analyse_ekf(
        ulog: ULog, check_levels: Dict[str, float], multi_instance: int = 0,
        red_thresh: float = 1.0, amb_thresh: float = 0.5, min_flight_duration_seconds: float = 5.0,
        in_air_margin_seconds: float = 5.0, pos_checks_when_sensors_not_fused: bool = False) -> \
        Tuple[str, Dict[str, str], Dict[str, float], Dict[str, float]]:
    """
    :param ulog:
    :param check_levels:
    :param multi_instance:
    :param red_thresh:
    :param amb_thresh:
    :param min_flight_duration_seconds:
    :param in_air_margin_seconds:
    :param pos_checks_when_sensors_not_fused:
    :return:
    """

    try:
        estimator_states = ulog.get_dataset('estimator_states', multi_instance).data
    except:
        raise PreconditionError('could not find estimator_states instance', multi_instance)

    try:
        estimator_status = ulog.get_dataset('estimator_status', multi_instance).data
    except:
        raise PreconditionError('could not find estimator_status instance', multi_instance)

    try:
        estimator_status_flags = ulog.get_dataset('estimator_status_flags', multi_instance).data
    except:
        raise PreconditionError('could not find estimator_status_flags instance', multi_instance)

    try:
        _ = ulog.get_dataset('estimator_innovations', multi_instance).data
    except:
        raise PreconditionError('could not find estimator_innovations instance', multi_instance)

    try:
        in_air = InAirDetector(
            ulog, min_flight_time_seconds=min_flight_duration_seconds, in_air_margin_seconds=0.0)
        in_air_no_ground_effects = InAirDetector(
            ulog, min_flight_time_seconds=min_flight_duration_seconds,
            in_air_margin_seconds=in_air_margin_seconds)
    except Exception as e:
        raise PreconditionError(str(e))

    if in_air_no_ground_effects.take_off is None:
        raise PreconditionError('no airtime detected.')

    airtime_info = {
        'in_air_transition_time': round(in_air.take_off + in_air.log_start, 2),
        'on_ground_transition_time': round(in_air.landing + in_air.log_start, 2)}

    gps_fail_flags = get_gps_check_fail_flags(estimator_status)

    sensor_checks, innov_fail_checks = find_checks_that_apply(
        estimator_status_flags, estimator_status,
        pos_checks_when_sensors_not_fused=pos_checks_when_sensors_not_fused)

    metrics = calculate_ecl_ekf_metrics(
        ulog, estimator_status_flags, innov_fail_checks, sensor_checks, in_air, in_air_no_ground_effects,
        multi_instance, red_thresh=red_thresh, amb_thresh=amb_thresh)

    check_status, master_status = perform_ecl_ekf_checks(
        metrics, sensor_checks, innov_fail_checks, check_levels)

    return master_status, check_status, metrics, airtime_info


def find_checks_that_apply(
    estimator_status_flags: dict, estimator_status: dict, pos_checks_when_sensors_not_fused: bool = False) ->\
        Tuple[List[str], List[str]]:
    """
    finds the checks that apply and stores them in lists for the std checks and the innovation
    fail checks.
    :param estimator_status_flags:
    :param estimator_status:
    :param b_pos_only_when_sensors_fused:
    :return: a tuple of two lists that contain strings for the std checks and for the innovation
    fail checks.
    """
    sensor_checks = list()
    innov_fail_checks = list()

    # Height Sensor Checks
    sensor_checks.append('hgt')
    innov_fail_checks.append('posv')

    # Magnetometer Sensor Checks
    if (np.amax(estimator_status_flags['cs_yaw_align']) > 0.5):
        sensor_checks.append('mag')

        innov_fail_checks.append('magx')
        innov_fail_checks.append('magy')
        innov_fail_checks.append('magz')
        innov_fail_checks.append('yaw')

    # Velocity Sensor Checks
    if (np.amax(estimator_status_flags['cs_gnss_vel']) > 0.5):
        sensor_checks.append('vel')
        innov_fail_checks.append('velh')
        innov_fail_checks.append('velv')

    # Position Sensor Checks
    if (pos_checks_when_sensors_not_fused or (np.amax(estimator_status_flags['cs_gnss_pos']) > 0.5)
        or (np.amax(estimator_status_flags['cs_ev_pos']) > 0.5)):
        sensor_checks.append('pos')
        innov_fail_checks.append('posh')

    # Airspeed Sensor Checks
    # a value > 1.0 means the measurement data for that test has been rejected by the EKF
    if (np.amax(estimator_status['tas_test_ratio']) > 0.0):
        sensor_checks.append('tas')
        innov_fail_checks.append('tas')

    # Height above ground (rangefinder) sensor checks
    if (np.amax(estimator_status['hagl_test_ratio']) > 0.0):
        sensor_checks.append('hagl')
        innov_fail_checks.append('hagl')

    # optical flow sensor checks
    if (np.amax(estimator_status_flags['cs_opt_flow']) > 0.5):
        innov_fail_checks.append('ofx')
        innov_fail_checks.append('ofy')

    return sensor_checks, innov_fail_checks