pyrfu.pyrf.match_phibe_v#

pyrfu.pyrf.match_phibe_v(b_0, b_z, int_e_dt, n, v)[source]#

Get propagation velocity by matching dBpar and phi. Used together with match_phibe_dir. Finds best match in amplitude given, B0, dB_par, phi, propagation direction implied, for specified n and v given as vectors. Returns a matrix of correlations and the two potentials that were correlated.

As irf_match_phibe_v.m, the “correlation” is the sum over time of \(\log_{10} |\phi_E / \phi_B|\), i.e., the number of samples times the mean logarithmic ratio of the potentials: the best match in amplitude is where it is closest to zero. Since \(\phi_E / \phi_B\) is proportional to \(n v\), only the product of the density and the velocity is determined.

Parameters:

  • b_0 (float) – Average background magnetic field [nT].

  • b_z (array_like) – Parallel wave magnetic field [nT].

  • int_e_dt (array_like) – Potential, time integral of the electric field in the propagation direction [mV/m s] (e.g., the best direction from match_phibe_dir).

  • n (array_like) – Vector of densities [cm^{-3}].

  • v (array_like) – Vector of velocities [km/s].

Returns:

  • corr_mat (numpy.ndarray) – Correlation matrix(nn x nv).

  • phi_b (numpy.ndarray) – B0 * dB_par / n_e * e * mu0 [V] (size: n_times x nn).

  • phi_e (numpy.ndarray) – int(E) dt * v(dl=-vdt = > -dl = vdt) [V] (size: n_times x nv).