def _downsample(b_xyz, dt):
delta_t = (b_xyz.time.data[-1] - b_xyz.time.data[0]).view("i8") * 1e-9
n_t = int(delta_t / dt)
timeline = b_xyz.time.data[0].copy()
timeline += (np.linspace(0, delta_t, n_t) * 1e9).astype(int)
timeline = ts_scalar(timeline, np.zeros(len(timeline)))
return resample(b_xyz, timeline)
def pressure_balance_b0(b_xyz=None, moments_i=None, moments_e=None):
"""Compute lobe magnetic field using pressure balance condition
Parameters
----------
b_xyz : xarray.DataArray
Time series of the magnetic field.
moments_i : list of xarray.DataArray
Time series of the moments of the ion VDF.
moments_e : list of xarray.DataArray
Time series of the moments of the electron VDF.
Returns
-------
b0 : xarray.DataArray
Time series of the lobe field.
"""
# Unpack pressure tensors
p_xyz_i, p_xyz_e = [moments_i[-1], moments_e[-1]]
# Compute total pressure tensor
p_xyz = p_xyz_i + resample(p_xyz_e, p_xyz_i)
# Transform to field aligned coordinates
p_xyzfac = rotate_tensor(p_xyz, "fac", b_xyz, "pp")
# Estimate magnetic field in the lobe using pressure balance
mu0 = constants.mu0.value # magnetic permittivity
p_th, p_b = [1e-9 * trace(p_xyzfac), 1e-18 * norm(b_xyz)**2 / (2 * mu0)]
# Compute plasma parameter
beta = resample(p_th, p_b) / p_b
# Compute lobe field using pressure balance
b0 = b_xyz * np.sqrt(1 + beta)
return b0
def st_derivative(r, b, mva, crossing_times):
"""
Computes velocity of the structure using spatio-temporal derivative method
"""
b_xyz = avg_4sc(b)
# Gradient of the magnetic field
grad_b = c_4_grad(r, b)
db_dt = gradient(b_xyz)
# Transform gradient to LMN frame
l_grad_b = np.matmul(grad_b.data, mva[:, 0])
m_grad_b = np.matmul(grad_b.data, mva[:, 1])
n_grad_b = np.matmul(grad_b.data, mva[:, 2])
# Compute velocity of the structure using MDD
v_str = np.zeros(db_dt.shape)
v_str[:, 0] = np.sum(db_dt * l_grad_b, axis=1) / np.linalg.norm(l_grad_b,
axis=1)**2
v_str[:, 1] = np.sum(db_dt * m_grad_b, axis=1) / np.linalg.norm(m_grad_b,
axis=1)**2
v_str[:, 2] = np.sum(db_dt * n_grad_b, axis=1) / np.linalg.norm(n_grad_b,
axis=1)**2
dt = calc_dt(b_xyz)
y_m = np.abs(np.cumsum(-v_str[:, 1]) * dt)
z_n = np.cumsum(-v_str[:, 2]) * dt
v_str = ts_vec_xyz(b_xyz.time.data, -v_str)
y_m = ts_scalar(b_xyz.time.data, y_m)
z_n = ts_scalar(b_xyz.time.data, z_n)
z_off = resample(t_eval(z_n, crossing_times), y_m)
z_n -= z_off
return v_str, y_m, z_n
def main(args):
"""main function
"""
with open(args.config) as f:
cfg = yaml.load(f, Loader=yaml.FullLoader)
tint = cfg["tint"]
# Spacecraft indices
mms_ids = np.arange(1, 5)
# Load spacecraft position and background magnetic field
r_mms = [
get_data("R_gse", tint, i, args.verbose, data_path=cfg["data_path"])
for i in mms_ids
]
# Load background magnetic field
suf_b = "fgm_{}_{}".format(cfg["fgm"]["data_rate"], cfg["fgm"]["level"])
b_mms = [
get_data("B_gse_{}".format(suf_b),
tint,
i,
args.verbose,
data_path=cfg["data_path"]) for i in mms_ids
]
# Remove offset on z component of the background magnetic field
b_mms = remove_bz_offset(b_mms)
# Compute current density
j_xyz, div_b, b_xyz, _, _, _ = c_4_j(r_mms, b_mms)
# j A.m^{-2}->nA.m^{-2}
j_xyz *= 1e9
# Load moments
moments_i, moments_e = load_moments(tint, cfg["fpi"], args,
cfg["data_path"])
# Resample moments to magnetic field sampling
moments_i = [resample(mom, b_xyz) for mom in moments_i]
moments_e = [resample(mom, b_xyz) for mom in moments_e]
lmn = lmn_cs(b_xyz, moments_i[1])
# Transform fields to MVA frame
b_lmn, j_lmn = [new_xyz(field, lmn) for field in [b_xyz, j_xyz]]
# Compute dispersion relation using either Jn of the timing
# Estimate phase velocity using normal current density
disprel_jn = calc_vph_current(b_lmn, j_lmn)
# Load timing data
timing_lr = load_timing(args.timing)
timing_ts = load_timing(args.table_wei2019)
# Unpack slowness vector
m_xyz, _ = [timing_lr.m, timing_lr.dm]
m_lmn = new_xyz(m_xyz, lmn)
n_lmn = new_xyz(timing_lr.n, lmn)
delta = np.arccos(n_lmn[:, 1] / np.linalg.norm(n_lmn.data[:, 1:], axis=1))
v_lmn = timing_lr.v.data[:, None] * n_lmn
v_n = np.linalg.norm(v_lmn[:, 1:], axis=1)
v_ph = v_n / np.cos(delta)
v_ph[delta > np.pi / 2 - .01] = np.nan
dv_ph = timing_lr.dv / np.cos(delta)
dv_ph[delta > np.pi / 2 - .01] = np.nan
# Crossing times
crossing_times = m_xyz.time.data
# Dispersion relation from timing
disprel_lr = calc_disprel_tm(v_ph,
dv_ph,
2 * timing_lr.tau,
timing_lr.dtau,
ci=95)
disprel_ts = calc_disprel_tm(timing_ts.v, timing_ts.dv, 2 * timing_ts.tau,
timing_ts.dtau)
# d_i = 527.0
# Compute thickness of the CS using the lobe field corresponding to the
# Harris like CS
# Compute lobe field using Harris current sheet fit
harris_fit = fit_harris_cs(b_lmn, j_lmn)
# Compute thickness as h = B0/curl(B) = B0/(m0*J) and the error
mu0 = constants.mu_0
# continuous thickness
h_c = norm(harris_fit.B0) / (1e3 * mu0 * norm(j_lmn))
# discrete thickness
h_d = t_eval(h_c, crossing_times)
# curl of the magnetic field
curl_b = c_4_grad(r_mms, b_mms, "curl")
# Errors
# relative error on curlometer technique
dj_j = np.abs(div_b / norm(curl_b))
# relative error on the thickness
dh_d = h_d * t_eval(dj_j / (1 + dj_j), crossing_times)
# Thickness from Wei 2019 table
h_ts, dh_ts = [timing_ts.h, timing_ts.dh]
# Compute ion inertial length to normalize the thickness
# unpack number densities
n_i, n_e = moments_i[0], moments_e[0]
# Unpack ion/electron temperature
_, _, t_i = dec_temperature(b_xyz, moments_i)
_, _, t_e = dec_temperature(b_xyz, moments_i)
# Compute plasma parameters
plasma_params = plasma_calc(harris_fit.B0, t_i, t_e, n_i, n_e)
# Averaged ion inertial length
d_i = np.mean(plasma_params.l_i).data
r_p = np.mean(plasma_params.rho_p).data
r_e = np.mean(plasma_params.rho_e).data
# Outliers indices
ols = cfg["outliers"]
# Fit scaling wavelength CS thickness
scaling_lr = scaling_h_lambda(h_d[~np.isnan(v_ph)], dh_d[~np.isnan(v_ph)],
disprel_lr, ols["Richard2021"])
scaling_ts = scaling_h_lambda(h_ts, dh_ts, disprel_ts, ols["Wei2019"])
fig6 = plt.figure(**cfg["figure"]["main"])
gs6 = fig6.add_gridspec(2, 2, **cfg["figure"]["gridspec"])
gs60 = gs6[:, 0].subgridspec(2, 1)
gs61 = gs6[:, 1].subgridspec(2, 1)
axs0 = [fig6.add_subplot(gs60[i]) for i in range(2)]
axs1 = [fig6.add_subplot(gs61[i]) for i in range(2)]
axs0[0], caxs00 = plot_spectr(axs0[0],
disprel_jn.hist,
cscale="log",
cmap="viridis")
axs0[0].plot(disprel_jn.hires_dBdt, disprel_jn.pred_Jn, "k")
axs0[0].fill_between(disprel_jn.hires_dBdt,
disprel_jn.bound_upper,
disprel_jn.bound_lower,
color="lightgrey")
labels = [
"$J_N=${:3.2f}*d$_t B_L$".format(disprel_jn.fit_db_dt_jn.data),
"95% CI"
]
axs0[0].legend(labels, frameon=True, loc="upper right")
axs0[0].set_xlabel("d$_t B_L$ [nT s$^{-1}$]")
axs0[0].set_ylabel("$J_N$ [nA m$^{-2}$]")
caxs00.set_ylabel("Counts")
axs0[0].grid(True, which="both")
axs0[0].set_title("$\\rho = {:3.2f}$".format(disprel_jn.rho.data))
h_lambda = scaling_lr.h.data * scaling_lr.k.data / (2 * np.pi)
dh_lambda = scaling_lr.h.data * scaling_lr.dk.data / (2 * np.pi)
n_bins = optimize_nbins_1d(h_lambda)
"""
y, edges_ = np.histogram(h_lambda, bins=n_bins)
axs0[1].bar(0.5 * (edges_[1:] + edges_[:-1]), y,
width=edges_[1:] - edges_[:-1], facecolor='tab:blue',
edgecolor="k", yerr=np.sqrt(y))
"""
#axs0[1].axvspan(0.8 / (2 * np.pi), 1.8 / (2 * np.pi), color="tab:orange")
_, _, _ = axs0[1].hist(h_lambda, bins=n_bins, **cfg["figure"]["hist"])
#axs0[1].errorbar(scaling_lr.scaling.data / (2 * np.pi), 16.5,
# xerr=scaling_lr.sigma_scaling.data / (2 * np.pi),)
axs0[1].axvline(scaling_lr.scaling.data / (2 * np.pi),
color="k",
linestyle="-")
axs0[1].set_ylim([0, 17])
axs0[1].set_yticks(np.arange(0, 17, 2))
left_bound = (scaling_lr.scaling.data -
1.96 * scaling_lr.sigma_scaling.data) / (2 * np.pi)
right_bound = (scaling_lr.scaling.data +
1.96 * scaling_lr.sigma_scaling.data) / (2 * np.pi)
width = right_bound - left_bound
_, ymax = axs0[1].get_ylim()
height = .5
rect_drift = plt.Rectangle((0.8 / (2 * np.pi), ymax - .5),
1. / (2 * np.pi),
.5,
color='tab:orange')
rect_sigma = plt.Rectangle((left_bound, ymax - 2.1 * height),
width,
height,
color='lightgrey')
axs0[1].add_patch(rect_drift)
axs0[1].add_patch(rect_sigma)
axs0[1].set_xlabel("$h/\\lambda$")
axs0[1].set_ylabel("Counts")
axs0[1].grid(True, which="both")
axs1[0].errorbar(disprel_lr.k.data,
disprel_lr.omega.data,
disprel_lr.omega_err.data,
disprel_lr.k_err.data,
color="tab:blue",
**cfg["figure"]["errorbar"])
axs1[0].errorbar(disprel_ts.k.data,
disprel_ts.omega.data,
disprel_ts.omega_err.data,
disprel_ts.k_err.data,
color="tab:red",
**cfg["figure"]["errorbar"])
axs1[0].errorbar(disprel_lr.k.data[ols["Richard2021"]],
disprel_lr.omega.data[ols["Richard2021"]],
disprel_lr.omega_err.data[ols["Richard2021"]],
disprel_lr.k_err.data[ols["Richard2021"]],
color="k",
**cfg["figure"]["errorbar"])
axs1[0].plot(disprel_lr.hires_k.data, disprel_lr.pred_omega.data, 'k-')
axs1[0].fill_between(disprel_lr.hires_k.data,
disprel_lr.bound_lower.data,
disprel_lr.bound_upper.data,
color="lightgrey")
axs1[0].set_xlim([0, 2.6e-3])
axs1[0].set_ylim([0, 1])
axs1[0].set_xlabel("$k$ [km$^{-1}$]")
axs1[0].set_ylabel("$\\omega$ [rad s$^{-1}$]")
labels = [
"$\\omega$ = {:4.2f}*$k$".format(disprel_lr.vph.data[0]), "95% CI",
"2019-09-14", "Table Wei2019"
]
axs1[0].legend(labels, frameon=True, loc="upper right")
axs1[0].yaxis.set_label_position("right")
axs1[0].yaxis.tick_right()
axs1[0].grid(True, which="both")
axs1[1].errorbar(scaling_lr.k,
scaling_lr.h,
scaling_lr.dh,
scaling_lr.dk,
color="tab:blue",
**cfg["figure"]["errorbar"])
axs1[1].errorbar(scaling_ts.k,
scaling_ts.h,
scaling_ts.dh,
scaling_ts.dk,
color="tab:red",
**cfg["figure"]["errorbar"])
axs1[1].errorbar(scaling_lr.k_ols,
scaling_lr.h_ols,
scaling_lr.dh_ols,
scaling_lr.dk_ols,
color="k",
**cfg["figure"]["errorbar"])
axs1[1].plot(scaling_lr.hires_k, scaling_lr.pred_h, "k")
axs1[1].fill_between(scaling_lr.hires_k,
scaling_lr.bound_lower,
scaling_lr.bound_upper,
color="lightgrey")
axs1[1].set_xlabel("$k$ [km$^{-1}$]")
axs1[1].set_ylabel("$h$ [km]")
labels = [
f"$h=${scaling_lr.scaling.data / (2 * np.pi):3.2f}$*\\lambda$",
"95% CI", "2019-09-14", "Table Wei2019"
]
axs1[1].legend(labels, frameon=True, loc="upper right")
axs1[1].plot(scaling_lr.hires_k,
0.8 / scaling_lr.hires_k,
color="tab:orange")
axs1[1].plot(scaling_lr.hires_k,
1.8 / scaling_lr.hires_k,
color="tab:orange")
axs1[1].yaxis.set_label_position("right")
axs1[1].yaxis.tick_right()
axs1[1].set_xlim([0, 2.6e-3])
axs1[1].set_ylim([0, 9000])
axs1[1].grid(True, which="both")
# Add panels labels
labels_pos = [0.032, 0.95]
_ = make_labels(axs0 + axs1, labels_pos)
if args.figname:
fig6.savefig(args.figname, **cfg["figure"]["save"])
else:
plt.show()
if args.verbose:
b_lobe = harris_fit.b_lobe.data
sigma_b_lobe = harris_fit.sigma_b_lobe.data
h_h = harris_fit.scale.data / d_i
sigma_h_h = harris_fit.sigma_scale.data / d_i
a_j = disprel_jn.fit_db_dt_jn.data
sigma_a_j = disprel_jn.sigma.data
v_ph_j = 1e-3 / (constants.mu_0 * a_j)
sigma_v_ph_j = v_ph_j * (sigma_a_j / a_j) / (1 + (sigma_a_j / a_j))
v_ph = disprel_lr.vph.data[0]
sigma_v_ph = disprel_lr.sigma_k_w.data[0]
kh_ = scaling_lr.scaling.data
sigma_kh = scaling_lr.sigma_scaling.data
h_l = kh_ / (2 * np.pi)
sigma_h_l = sigma_kh / (2 * np.pi)
h_min = 1e3 * np.min(h_d.data)
l_min = 1e3 * np.min(disprel_lr.lamb.data)
l_lh = 2 * np.pi * np.sqrt(r_p * r_e)
print(f"B_0 = {b_lobe:3.2f} \pm {sigma_b_lobe:3.2f} nT")
print(f"h_H = {h_h:3.2f} \pm {sigma_h_h:3.2f} d_i")
print(f"a = {a_j:3.2f} \pm {sigma_a_j:3.2f} s/H")
print(f"v_ph = {v_ph_j:3.2f} \pm {sigma_v_ph_j:3.2f} km/s")
print(f"v_ph = {v_ph:3.2f} \pm {sigma_v_ph:3.2f} km/s")
print(f"kh = {kh_:3.2f} \pm {sigma_kh:3.2f}")
print(f"h/\lambda = {h_l:3.2f} \pm {sigma_h_l:3.2f}")
print(f"\lambda_min = {(l_min / d_i):3.2f} d_i")
print(f"\lambda_min = {(l_min / r_p):3.2f} r_i")
print(f"\lambda_LH = {(l_lh / d_i):3.2f}")
print(f"h_min = {(h_min / d_i):3.2f} d_i")
print(f"h_min = {(h_min / r_p):3.2f} r_i")
def main(args):
"""main function
"""
with open(args.config) as f:
cfg = yaml.load(f, Loader=yaml.FullLoader)
tint = cfg["tints"]["flapping"]
# Spacecraft indices
mms_ids = np.arange(1, 5)
# Load spacecraft position and background magnetic field
r_mms = [get_data("R_gse", tint, i,
data_path=cfg["data_path"]) for i in mms_ids]
# Load background magnetic field
suf_b = "fgm_{}_{}".format(cfg["fgm"]["data_rate"], cfg["fgm"]["level"])
b_mms = [get_data("B_gse_{}".format(suf_b), tint, i, args.verbose,
data_path=cfg["data_path"]) for i in mms_ids]
# Remove offset on z component of the background magnetic field
b_mms = remove_bz_offset(b_mms)
# Load moments
moments_i, moments_e = load_moments(tint, cfg["fpi"], args,
cfg["data_path"])
# Compute current density
j_xyz, div_b, b_xyz, _, _, _ = c_4_j(r_mms, b_mms)
j_xyz *= 1e9
# Compute MVA frame
_, _, lmn = mva(b_xyz)
# Resample moments to magnetic field sampling
moments_i = [resample(mom, b_xyz) for mom in moments_i]
moments_e = [resample(mom, b_xyz) for mom in moments_e]
# lmn = np.vstack([lmn[:, 0], -lmn[:, 2], lmn[:, 1]]).T
l = lmn[:, 0]
m = np.mean(moments_i[1].data, axis=0)
m /= np.linalg.norm(m, axis=0)
n = np.cross(l, m) / np.linalg.norm(np.cross(l, m))
m = np.cross(n, l)
lmn = np.transpose(np.vstack([l, m, n]))
# transform magnetic field and current density to LMN coordinates system
b_lmn, j_lmn = [new_xyz(field, lmn) for field in [b_xyz, j_xyz]]
# Load data from timing
timing_lr = load_timing(args.timing)
# Transform slowness vector to LMN frame
m_lmn, dm_lmn = [new_xyz(vec, lmn) for vec in [timing_lr.m, timing_lr.dm]]
# Transform normal from timing to LMN coordinates system
n_lmn = new_xyz(timing_lr.n, lmn)
slowness = xr.Dataset({"m": m_lmn, "dm": dm_lmn})
# Get crossing times
crossing_times = m_lmn.time.data
# Unpack ion/electron temperature
_, _, t_i = dec_temperature(b_xyz, moments_i)
_, _, t_e = dec_temperature(b_xyz, moments_i)
v_xyz_i = moments_i[1]
v_xyz_e = moments_e[1]
v_lmn_i, v_lmn_e = [new_xyz(v_xyz, lmn) for v_xyz in [v_xyz_i, v_xyz_e]]
# Compute velocity and geometry of the CS using spatio-temporal derivative
b_mms_ds = [_downsample(b_xyz, 2.5) for b_xyz in b_mms]
#v_str_lmn, y_m, z_n = st_derivative(r_mms, b_mms, lmn, crossing_times)
v_str_lmn, y_m, z_n = st_derivative(r_mms, b_mms_ds, lmn, crossing_times)
# filter velocity of the CS
# change 257 to physical value
#v_str_lmn_filtered = medfilt(v_str_lmn, 100)
grad_b = c_4_grad(r_mms, b_mms_ds)
grad_b_dete = ts_scalar(grad_b.time.data,
np.abs(np.linalg.det(grad_b.data)))
res_dete = grad_b_dete.data - medfilt(grad_b_dete, 5).data
idx_ = np.abs(res_dete) > np.std(res_dete)
v_str_lmn_filtered = v_str_lmn.copy()
v_str_lmn_filtered.data[idx_, 1] = np.nan
v_str_lmn_filtered.data[idx_, 2] = np.nan
# Plot
fig, axs = plt.subplots(4, **cfg["figure"]["main"])
fig.subplots_adjust(**cfg["figure"]["subplots"])
plot_line(axs[0], b_lmn)
axs[0].legend(["$B_L$", "$B_M$", "$B_N$"], **cfg["figure"]["legend"])
axs[0].set_ylabel("$B$ [nT]")
axs[0].grid(True, which="both")
axs[1].quiver(timing_lr.tau.time.data, np.zeros(len(n_lmn)),
n_lmn[:, 1], n_lmn[:, 2],
color="tab:green", angles="uv")
axs[1].set_ylabel("$n_{timing}^{MN}$")
axs[1].grid(True, which="both")
plot_line(axs[2], v_lmn_i[:, 1], color="tab:blue", label="Ions")
plot_line(axs[2], v_str_lmn_filtered[:, 1], color="k", label="STD")
axs[2].errorbar(slowness.time.data, slowness.m.data[:, 1],
slowness.dm.data[:, 1],
color="tab:green", label="Timing")
axs[2].legend(**cfg["figure"]["legend"])
axs[2].set_ylim([-650, 650])
axs[2].set_ylabel("$V_M$ [km s$^{-1}$]")
axs[2].grid(True, which="both")
plot_line(axs[3], v_lmn_e[:, 2], color="tab:red", label="Electrons")
plot_line(axs[3], v_str_lmn_filtered[:, 2], color="k", label="STD")
axs[3].errorbar(slowness.time.data, slowness.m.data[:, 2],
slowness.dm.data[:, 2],
color="tab:green", label="Tining")
axs[3].legend(**cfg["figure"]["legend"])
axs[3].set_ylim([-650, 650])
axs[3].set_ylabel("$V_N$ [km s$^{-1}$]")
axs[3].grid(True, which="both")
axs[-1].set_xlim(mdates.date2num(tint))
fig.align_ylabels(axs)
labels_pos = [0.02, 0.92]
_ = make_labels(axs, labels_pos)
if args.figname:
fig.savefig(args.figname, **cfg["figure"]["save"])
else:
plt.show()
def main(args):
"""main function
"""
with open(args.config) as f:
cfg = yaml.load(f, Loader=yaml.FullLoader)
# Spacecraft indices
mms_ids = np.arange(1, 5)
# Load spacecraft position and background magnetic field
r_mms = [get_data("R_gse", cfg["tint"], i,
data_path=cfg["data_path"]) for i in mms_ids]
# Load background magnetic field
suf_b = "fgm_{}_{}".format(cfg["fgm"]["data_rate"], cfg["fgm"]["level"])
b_mms = [get_data("B_gse_{}".format(suf_b), cfg["tint"], i, args.verbose,
data_path=cfg["data_path"]) for i in mms_ids]
# Remove offset on z component of the background magnetic field
b_mms = remove_bz_offset(b_mms)
# Load moments
moments_i, moments_e = load_moments(cfg["tint"], cfg["fpi"], args,
cfg["data_path"])
# Compute current density
j_xyz, div_b, b_xyz, _, _, _ = c_4_j(r_mms, b_mms)
j_xyz *= 1e9 # j A.m^{-2}->nA.m^{-2}
# Resample moments to magnetic field sampling
moments_i = [resample(mom, b_xyz) for mom in moments_i]
moments_e = [resample(mom, b_xyz) for mom in moments_e]
# Compute new coordinates system
lmn = lmn_cs(b_xyz, moments_i[1])
# transform magnetic field and current density to LMN coordinates system
b_lmn, j_lmn = [new_xyz(field, lmn) for field in [b_xyz, j_xyz]]
# Fit J perp vs B as Harris like CS
harris_fit = fit_harris_cs(b_lmn, j_lmn)
# Load data from timing
timing_lr = load_timing(args.timing)
# Transform slowness vector to LMN frame
m_lmn = new_xyz(timing_lr.m, lmn)
# Get crossing times
crossing_times = m_lmn.time.data
# Use Harris like CS lobe field to estimate the thickness
# Compute thickness as h = B0/curl(B) = B0/(m0*J) and the error
mu0 = constants.mu_0
# continuous thickness
h_c = norm(harris_fit.B0) / (1e3 * mu0 * norm(j_lmn))
# discrete thickness
h_d = t_eval(h_c, crossing_times)
# Errors
# relative error on curlometer technique
dj_j = np.abs(div_b / (mu0 * norm(1e-9 * j_xyz)))
# relative error on the thickness
dh_d = h_d * t_eval(dj_j / (1 + dj_j), crossing_times)
# Compute ion inertial length to normalize the thickness
# unpack number densities
n_i, n_e = moments_i[0], moments_e[0]
# Unpack ion/electron temperature
_, _, t_i = dec_temperature(b_xyz, moments_i)
_, _, t_e = dec_temperature(b_xyz, moments_i)
# Compute plasma parameters
plasma_params = plasma_calc(harris_fit.B0, t_i, t_e, n_i, n_e)
# Averaged ion inertial length
d_i = 1e-3 * np.mean(plasma_params.l_i).data
r_p = 1e-3 * np.mean(plasma_params.rho_p).data
r_e = 1e-3 * np.mean(plasma_params.rho_e).data
# Normalize thickness by ion inertial length
h_d, dh_d = [h_d / d_i, dh_d / d_i]
# Compute velocity and geometry of the CS using spatio-temporal derivative
v_str_lmn, y_m, z_n = st_derivative(r_mms, b_mms, lmn, crossing_times)
geometry = xr.Dataset({"y_m": y_m / d_i, "z_n": z_n / d_i})
# Compute ym and zn at the crossings
crossing_times = h_d.time.data
yc_m, zc_n = [t_eval(x, crossing_times) for x in [geometry.y_m, geometry.z_n]]
fig = plt.figure(**cfg["figure"]["main"])
gs0 = fig.add_gridspec(4, 2, **cfg["figure"]["gridspec"])
gs10 = gs0[:2, 0].subgridspec(1, 1)
gs20 = gs0[:2, 1].subgridspec(2, 1, hspace=.4)
gs30 = gs0[2:, :].subgridspec(1, 1)
axs0 = [fig.add_subplot(gs10[i]) for i in range(1)]
axs1 = [fig.add_subplot(gs20[i]) for i in range(2)]
axs2 = [fig.add_subplot(gs30[i]) for i in range(1)]
axs0[0], caxs0 = plot_spectr(axs0[0], harris_fit.hist,
cscale="log", cmap="viridis")
axs0[0].errorbar(harris_fit.bbins, harris_fit.medbin,
harris_fit.medstd, marker="s", color="tab:red")
axs0[0].plot(harris_fit.hires_b, harris_fit.pred_j_perp,
color="tab:red", linestyle="--", linewidth=2)
axs0[0].set_ylim([0, 18])
axs0[0].set_xlabel("$B_L$ [nT]")
axs0[0].set_ylabel("$J_{MN}$ [nA m$^{-2}$]")
caxs0.set_ylabel("Counts")
labels0 = ["Harris fit $B_0$={:2.0f} nT".format(harris_fit.B0.data[0, 0]),
"median"]
axs0[0].legend(labels0, **cfg["figure"]["legend"])
for ax in axs1:
ax.errorbar(yc_m.data, zc_n.data, h_d.data / 2, **cfg["figure"][
"errorbar"])
ax.plot(geometry.y_m.data, geometry.z_n.data, "tab:blue")
ax.set_ylim([-6, 6])
ax.set_xlabel("$M/d_i$")
ax.set_ylabel("$N/d_i$")
axs1[0].legend(["CS", "h"], **cfg["figure"]["legend"])
axs1[1].set_xticks(yc_m.data)
axs1[0].set_xlim([0, np.max(geometry.y_m.data)])
axs1[1].set_xlim([70, 105])
axs1[0].grid(True, which="both")
axs1[1].grid(True, which="both")
zoom(axs1[1], axs1[0], ec="k")
# Label the panels
axs0[0].text(0.02, 0.95, "({})".format("a"), transform=axs0[0].transAxes)
axs1 = make_labels(axs1, [0.02, 0.86], 1)
axs2[0].text(0.01, 0.95, "({})".format(string.ascii_lowercase[len(axs1) + 1]),
transform=axs2[0].transAxes)
axs2[0].axis("off")
if args.figname:
fig.savefig(args.figname, **cfg["figure"]["save"])
else:
plt.show()
def main(args):
"""main function
"""
# Read time intervals
with open(args.config) as f:
cfg = yaml.load(f, Loader=yaml.FullLoader)
tint_flap = cfg["tints"]["flapping"]
tint_zoom = cfg["tints"]["close-up"]
mms_ids = np.arange(1, 5)
# Load spacecraft position and background magnetic field
r_mms = [
get_data("R_gse",
tint_flap,
i,
args.verbose,
data_path=cfg["data_path"]) for i in mms_ids
]
suf_b = "fgm_{}_{}".format(cfg["fgm"]["data_rate"], cfg["fgm"]["level"])
b_mms = [
get_data("B_gse_{}".format(suf_b),
tint_flap,
i,
args.verbose,
data_path=cfg["data_path"]) for i in mms_ids
]
# Remove offset on z component of the background magnetic field
b_mms = remove_bz_offset(b_mms)
# Compute magnetic field at the center of mass of the tetrahedron
j_xyz, _, b_xyz, _, _, _ = c_4_j(r_mms, b_mms)
j_xyz *= 1e9 # j A.m^{-2}->nA.m^{-2}
# Compute magnetic field at the center of mass of the tetrahedron
b_xyz = avg_4sc(b_mms)
# Compute MVA frame
b_lmn, _, lmn = mva(b_xyz)
# Correct minimum variance frame
# lmn = np.vstack([lmn[:, 0], -lmn[:, 2], lmn[:, 1]]).T
l = lmn[:, 0]
m = np.mean(j_xyz.data, axis=0) / np.linalg.norm(np.mean(j_xyz, axis=0))
n = np.cross(l, m) / np.linalg.norm(np.cross(l, m))
m = np.cross(n, l)
lmn = np.transpose(np.vstack([l, m, n]))
b_xyz = time_clip(b_mms[args.mms_id - 1], tint_zoom)
b_lmn = new_xyz(b_xyz, lmn)
# Load electric field and magnetic field fluctuation
# Load electric field
suf_e = "edp_{}_{}".format(cfg["edp"]["data_rate"], cfg["edp"]["level"])
e_xyz = get_data("e_gse_{}".format(suf_e),
tint_zoom,
args.mms_id,
args.verbose,
data_path=cfg["data_path"])
# Load electric field
suf_db = "scm_{}_{}".format(cfg["scm"]["data_rate"], cfg["scm"]["level"])
b_scm = get_data("b_gse_{}".format(suf_db),
tint_zoom,
args.mms_id,
args.verbose,
data_path=cfg["data_path"])
# Load density and pressure tensor
moments_i, moments_e = load_moments(tint_flap,
cfg["fpi"],
args,
data_path=cfg["data_path"])
# Unpack moments
_, _, _, p_xyz_i = moments_i
n_e, _, _, p_xyz_e = moments_e
# Total pressure tensor
p_xyz = p_xyz_i + resample(p_xyz_e, p_xyz_i)
# Convert to field-aligned coordinates
p_xyzfac = rotate_tensor(p_xyz, "fac", b_xyz, "pp")
# Permittivity
mu0 = constants.mu_0
# Thermal pressure (scalar)
pth = 1e-9 * trace(p_xyzfac) / 3.
# Magnetic pressure
p_mag = 1e-18 * norm(b_xyz)**2 / (2 * mu0)
# Plasma beta
beta = resample(pth, b_xyz) / p_mag
# Plasma parameters
plasma_params = plasma_calc(b_xyz, n_e, n_e, n_e, n_e)
# Convert electric field and magnetic field fluctuation to field aligned
# coordinates
e_xyzfac = convert_fac(e_xyz, b_xyz, [1, 0, 0])
# bandpass filter electric field and magnetic field fluctuation
f_min = 4
e_xyzfac_hf = filt(e_xyzfac, f_min, 0, 3)
cwt_options = dict(nf=100, f=[0.5, 1e3], plot=False)
# Compute continuous wavelet transform of the electric field
e_xyzfac_cwt = wavelet(e_xyzfac, **cwt_options)
# Construct compressed spectrogram of E
e_cwt_times, e_cwt_x, e_cwt_y, e_cwt_z = compress_cwt(e_xyzfac_cwt, nc=100)
e_cwt = xr.DataArray(e_cwt_x + e_cwt_y + e_cwt_z,
coords=[e_cwt_times, e_xyzfac_cwt.frequency],
dims=["time", "frequency"])
# Compute continuous wavelet transform of the magnetic field fluctuations
b_scm_cwt = wavelet(b_scm, **cwt_options)
# Construct compressed spectrogram of E
b_cwt_times, b_cwt_x, b_cwt_y, b_cwt_z = compress_cwt(b_scm_cwt, nc=100)
b_cwt = xr.DataArray(b_cwt_x + b_cwt_y + b_cwt_z,
coords=[b_cwt_times, b_scm_cwt.frequency],
dims=["time", "frequency"])
# Plot
fig, axs = plt.subplots(4, sharex="all", figsize=(6.5, 9))
fig.subplots_adjust(bottom=.05, top=.95, left=.15, right=.85, hspace=0.)
kwargs_legend = dict(ncol=3, loc="upper right", frameon=True)
kwargs_spectr = dict(cscale="log",
yscale="log",
cmap="Spectral_r",
clim=[1e-7, 1e0])
plot_line(axs[0], b_lmn)
axs[0].legend(["$B_L$", "$B_M$", "$B_N$"], **cfg["figure"]["legend"])
axs[0].set_ylabel("$B$ [nT]")
axs[0].grid(True, which="both")
plot_line(axs[1], e_xyzfac_hf)
labels = ["$E_{\\perp 1}$", "$E_{\\perp 2}$", "$E_{\\parallel}$"]
axs[1].legend(labels, **cfg["figure"]["legend"])
axs[1].set_ylabel("$E$ [mV m$^{-1}$]")
axs[1].set_ylim([-9, 9])
axs[1].set_yticks([-6, 0, 6])
axs1b = axs[1].twinx()
plot_line(axs1b, beta, color="k")
axs1b.set_yscale("log")
axs1b.set_ylabel("$\\beta$")
axs1b.set_ylim([10**(.5) * 1e0, 10**(.5) * 1e3])
axs[1].grid(True, which="both")
axs[2], caxs2 = plot_spectr(axs[2], e_cwt, **cfg["figure"]["spectrum"])
plot_line(axs[2], plasma_params.f_lh, color="k", label="$f_{lh}$")
plot_line(axs[2], plasma_params.f_ce, color="w", label="$f_{ce}$")
plot_line(axs[2], plasma_params.f_pp, color="red", label="$f_{pi}$")
axs[2].set_ylim([0.5, 1e3])
axs[2].set_ylabel("$f$ [Hz]")
caxs2.set_ylabel("$E^2$ " + "\n" + "[mV$^2$ m$^{-2}$ Hz$^{-1}$]")
axs[2].legend(**cfg["figure"]["legend"])
axs[3], caxs3 = plot_spectr(axs[3], b_cwt, **cfg["figure"]["spectrum"])
plot_line(axs[3], plasma_params.f_lh, color="k", label="$f_{lh}$")
plot_line(axs[3], plasma_params.f_ce, color="w", label="$f_{ce}$")
plot_line(axs[3], plasma_params.f_pp, color="red", label="$f_{pi}$")
axs[3].set_ylim([0.5, 1e3])
axs[3].set_ylabel("$f$ [Hz]")
caxs3.set_ylabel("$B^2$ " + "\n" + "[nT$^2$ Hz$^{-1}$]")
axs[3].legend(**cfg["figure"]["legend"])
fig.align_ylabels(axs)
axs[1].text(.85,
.15,
"$f > ${:2.1f} Hz".format(f_min),
transform=axs[1].transAxes)
# Time interval of the flapping
axs[-1].set_xlim(mdates.date2num(tint_zoom))
# Add panels labels
labels_pos = [0.02, 0.90]
_ = make_labels(axs, labels_pos)
if args.figname:
fig.savefig(args.figname, **cfg["figure"]["save"])
else:
plt.show()
def main(args):
"""main function
"""
# Read time intervals
with open(args.config) as f:
cfg = yaml.load(f, Loader=yaml.FullLoader)
tint_flap = cfg["tints"]["flapping"]
tint_zoom = cfg["tints"]["close-up"]
mms_ids = np.arange(1, 5)
# Load spacecraft position
r_mms = [
get_data("R_gse", tint_flap, i, data_path=cfg["data_path"])
for i in mms_ids
]
# Load background magnetic field
suf_b = "fgm_{}_{}".format(cfg["fgm"]["data_rate"], cfg["fgm"]["level"])
b_mms = [
get_data("B_gse_{}".format(suf_b),
tint_flap,
i,
args.verbose,
data_path=cfg["data_path"]) for i in mms_ids
]
# Load electric field
suf_e = "edp_{}_{}".format(cfg["edp"]["data_rate"], cfg["edp"]["level"])
e_mms = [
get_data("e_gse_{}".format(suf_e),
tint_flap,
i,
args.verbose,
data_path=cfg["data_path"]) for i in mms_ids
]
# Load moments from FPI
moments_i, moments_e = load_moments(tint_flap,
cfg["fpi"],
args,
data_path=cfg["data_path"])
# Remove offset on z component of the background magnetic field
b_mms = remove_bz_offset(b_mms)
# Compute current density
j_xyz, _, b_xyz, _, _, _ = c_4_j(r_mms, b_mms)
j_xyz *= 1e9 # j A.m^{-2}->nA.m^{-2}
# Compute electric field at the center of mass of the tetrahedron
e_xyz = avg_4sc(e_mms)
# Compute new coordinates system
lmn = lmn_cs(b_xyz, moments_i[1])
# Resample magnetic field and current density to electric field sampling
b_xyz, j_xyz = [resample(field, e_xyz) for field in [b_xyz, j_xyz]]
# Transform fields to MVA frame
b_lmn, e_lmn, j_lmn = [new_xyz(x_, lmn) for x_ in [b_xyz, e_xyz, j_xyz]]
# Resample ion/electron moments to electric field sampling
moments_i = [resample(mom, e_xyz) for mom in moments_i]
moments_e = [resample(mom, e_xyz) for mom in moments_e]
# Transform moments
moments_lmn_i = [moments_i[0], None, None, None]
moments_lmn_e = [moments_e[0], None, None, None]
moments_lmn_i[1:] = [new_xyz(mom, lmn) for mom in moments_i[1:]]
moments_lmn_e[1:] = [new_xyz(mom, lmn) for mom in moments_e[1:]]
# Compute terms of the Ohm's law
vxb_lmn_i, vxb_lmn_e, jxb_lmn = calc_ol_terms(b_lmn, j_lmn, moments_lmn_i,
moments_lmn_e)
# Compute ion scale total contribution
s_lmn = -vxb_lmn_i + jxb_lmn
# Plot
fig = plt.figure(**cfg["figure"]["main"])
gs0 = fig.add_gridspec(2, 1, **cfg["figure"]["gridspec"])
gs00 = gs0[:1].subgridspec(4, 1, hspace=0)
gs10 = gs0[1:].subgridspec(4, 1, hspace=0)
axs0 = [fig.add_subplot(gs00[i]) for i in range(4)]
axs1 = [fig.add_subplot(gs10[i]) for i in range(4)]
for axs in [axs0, axs1]:
plot_line(axs[0], b_lmn)
axs[0].legend(["$B_L$", "$B_M$", "$B_N$"], **cfg["figure"]["legend"])
axs[0].set_ylabel("$B$" + "\n" + "[nT]")
axs[0].grid(True, which="both")
plot_line(axs[1], jxb_lmn[:, 1], label="$J\\times B / ne$")
plot_line(axs[1], -vxb_lmn_i[:, 1], label="$-V_i\\times B$")
plot_line(axs[1], s_lmn[:, 1], label="$J\\times B / ne -V_i\\times B$")
plot_line(axs[1], e_lmn[:, 1], label="$E_{EDP}$")
axs[1].set_ylim([-12, 12])
axs[1].set_ylabel("$E_M$" + "\n" + "[mV m$^{-1}$]")
axs[1].grid(True, which="both")
plot_line(axs[2], jxb_lmn[:, 2], label="$J\\times B / ne$")
plot_line(axs[2], -vxb_lmn_i[:, 2], label="$-V_i\\times B$")
plot_line(axs[2], s_lmn[:, 2], label="$J\\times B / ne -V_i\\times B$")
plot_line(axs[2], e_lmn[:, 2], label="$E_{EDP}$")
axs[2].legend(frameon=True,
ncol=2,
bbox_to_anchor=(1, 1.3),
loc="upper right")
axs[2].set_ylim([-12, 12])
axs[2].set_ylabel("$E_N$" + "\n" + "[mV m$^{-1}$]")
axs[2].grid(True, which="both")
plot_line(axs[3],
norm(e_xyz + vxb_lmn_i),
color="deeppink",
label="$| E+V_{i}\\times B |$")
plot_line(axs[3],
norm(jxb_lmn),
color="tab:blue",
label="$|J\\times B / ne|$")
axs[3].legend(**cfg["figure"]["legend"])
axs[3].set_ylim([0, 12])
axs[3].set_ylabel("$|E|$" + "\n" + "[mV m$^{-1}$]")
axs[3].grid(True, which="both")
axs[-1].get_shared_x_axes().join(*axs)
fig.align_ylabels(axs)
for ax in axs[:-1]:
ax.xaxis.set_ticklabels([])
axs0[-1].set_xlim(mdates.date2num(tint_flap))
axs1[-1].set_xlim(mdates.date2num(tint_zoom))
# zoom
zoom(axs1[0], axs0[-1], ec="k")
span_options = dict(linestyle="--",
linewidth=0.8,
facecolor="none",
edgecolor="k")
span_tint(axs0, tint_zoom, **span_options)
# number subplots
labels_pos = [0.02, 0.83]
_ = make_labels(axs0 + axs1, labels_pos)
if args.figname:
fig.savefig(args.figname, **cfg["figure"]["save"])
else:
plt.show()
