Usage

Basic Usage

Instantiate the filter by passing a single inputs dictionary. Then call calculate() and use the available plotting helpers.

from mekf import MEKF

# Create MEKF instance with minimal inputs
inputs = {
    "t_max": 1000,
    "dt": 0.01,
    "sigma_startracker": 6,
    "sigma_v": 1e-6,
    "sigma_u": 1e-9,
    "freq_startracker": 1,
    "freq_gyro": 20,
    "init_inaccuracy": 30,
    "rng_seed": 1,
    # Optional: initial conditions and custom truth rate function
    # "B_h_0": np.array([0.0, 0.0, 0.0]),
    # "B_t_0": np.array([0.1, 0.1, 0.1]) * (np.pi / (180 * 3600)),
    # "q_t_0": np.array([1, 0, 0, 1]) / (2**0.5),
    # "w_t_fun": lambda t: ...
}

mekf = MEKF(inputs=inputs)
results = mekf.calculate()

# Plotting helpers (call any subset)
mekf.plot_pointing_error()
mekf.plot_bias()
mekf.plot_attitude()
mekf.plot_errors_with_bounds()
# Or all at once:
# mekf.plot_all()

Parameters

All previous top-level values from ekf.py are now provided inside the inputs dict:

• t_max (float)

• dt (float)

• sigma_startracker (float, arcsec)

• sigma_v (float, rad/s/sqrt(Hz))

• sigma_u (float, rad/s^1.5)

• freq_startracker (Hz)

• freq_gyro (Hz)

• init_inaccuracy (float)

• rng_seed (int)

• Joseph (bool)

• simple_Phi (bool)

• B_h_0 (3,)

• Pq (3x3)

• Pb (3x3)

• B_t_0 (3,)

• q_t_0 (4,)

• w_t_fun (callable)