def kinetic_entropy(sc, mode, start_date, end_date, **kwargs):
# Read the data
b = fgm.load_data(sc, mode, start_date, end_date)
dis_moms = fpi.load_moms(sc, mode, 'i', start_date, end_date)
des_moms = fpi.load_moms(sc, mode, 'e', start_date, end_date)
dis_dist = fpi.load_dist(sc, mode, 'i', start_date, end_date)
des_dist = fpi.load_dist(sc, mode, 'e', start_date, end_date)
# Equivalent Maxwellian distribution
dis_max_dist = fpi.maxwellian_distribution(dis_dist,
N=dis_moms['density'],
bulkv=dis_moms['velocity'],
T=dis_moms['t'])
des_max_dist = fpi.maxwellian_distribution(des_dist,
N=des_moms['density'],
bulkv=des_moms['velocity'],
T=des_moms['t'])
# Entropy density
si_dist = fpi.entropy(dis_dist, **kwargs)
se_dist = fpi.entropy(des_dist, **kwargs)
# si_max = fpi.maxwellian_entropy(dis_moms['density'], dis_moms['p'])
# se_max = fpi.maxwellian_entropy(des_moms['density'], des_moms['p'])
si_max = fpi.entropy(dis_max_dist, **kwargs)
se_max = fpi.entropy(des_max_dist, **kwargs)
# Velcoity space entropy density
siv_dist = fpi.vspace_entropy(dis_dist,
N=dis_moms['density'],
s=si_dist,
**kwargs)
sev_dist = fpi.vspace_entropy(des_dist,
N=des_moms['density'],
s=se_dist,
**kwargs)
siv_max = fpi.vspace_entropy(des_max_dist,
N=dis_moms['density'],
s=si_max,
**kwargs)
sev_max = fpi.vspace_entropy(des_max_dist,
N=des_moms['density'],
s=se_max,
**kwargs)
# M-bar
mi_bar = np.abs(si_max - si_dist) / si_max
me_bar = np.abs(se_max - se_dist) / se_max
miv_bar = np.abs(siv_max - siv_dist) / siv_max
mev_bar = np.abs(sev_max - sev_dist) / sev_max
# Epsilon
ei = fpi.epsilon(dis_dist,
N=dis_moms['density'],
V=dis_moms['velocity'],
T=dis_moms['t'],
**kwargs)
ee = fpi.epsilon(des_dist,
N=des_moms['density'],
V=des_moms['velocity'],
T=des_moms['t'],
**kwargs)
# Setup the plot
nrows = 9
ncols = 1
figsize = (5.5, 7.0)
fig, axes = plt.subplots(nrows=nrows,
ncols=ncols,
figsize=figsize,
squeeze=False)
# B
ax = axes[0, 0]
ax = util.plot([b['B'][:, 3], b['B'][:, 0], b['B'][:, 1], b['B'][:, 2]],
ax=ax,
labels=['|B|', 'Bx', 'By', 'Bz'],
xaxis='off',
ylabel='B\n(nT)')
# Ion entropy density
ax = axes[1, 0]
ax = util.plot([si_max, si_dist],
ax=ax,
labels=['$s_{i,max}$', '$s_{i}$'],
xaxis='off',
ylabel='s\n$J/K/m^{3}$ $ln(s^{3}/m^{6})$')
# Electron entropy density
ax = axes[2, 0]
ax = util.plot([se_max, se_dist],
ax=ax,
labels=['$s_{e,max}$', '$s_{e}$'],
xaxis='off',
ylabel='s')
# Ion M-bar
ax = axes[3, 0]
ax = util.plot(mi_bar,
ax=ax,
legend=False,
xaxis='off',
ylabel='$\overline{M}_{i}$')
# Ion Epsilon
ax = ax.twinx()
ei.plot(ax=ax, color='g')
ax.set_ylabel('$\epsilon_{i}$\n$(s/m)^{3/2}$')
ax.yaxis.label.set_color('g')
ax.tick_params(axis='y', colors='g')
ax.set_xticklabels([])
# Electron M-bar
ax = axes[4, 0]
ax = util.plot(me_bar,
ax=ax,
legend=False,
xaxis='off',
ylabel='$\overline{M}_{e}$')
# Electron Epsilon
ax = ax.twinx()
ee.plot(ax=ax, color='r')
ax.set_ylabel('$\epsilon_{e}$\n$(s/m)^{3/2}$')
ax.yaxis.label.set_color('r')
ax.tick_params(axis='y', colors='r')
ax.set_xticklabels([])
# Velocity space ion entropy density
ax = axes[5, 0]
ax = util.plot([siv_max, siv_dist],
ax=ax,
labels=['$s_{i,V,max}$', '$s_{i,V}$'],
xaxis='off',
ylabel='$s_{V}$\n$J/K/m^{3}$ $ln()$')
# Velocity space electron entropy density
ax = axes[6, 0]
ax = util.plot([sev_max, sev_dist],
ax=ax,
labels=['$s_{e,V,max}$', '$s_{e,V}$'],
xaxis='off',
ylabel='$s_{V}$')
# Velocity space ion M-bar
ax = axes[7, 0]
ax = util.plot(miv_bar,
ax=ax,
legend=False,
xaxis='off',
ylabel='$\overline{M}_{i,V}$')
# Velocity space electron M-bar
ax = axes[8, 0]
ax = util.plot(mev_bar, ax=ax, legend=False, ylabel='$\overline{M}_{e,V}$')
fig.suptitle('Total and Velocity Space Entropy Density')
plt.subplots_adjust(left=0.2, right=0.85, top=0.95, hspace=0.4)
# plt.setp(axes, xlim=xlim)
return fig, axes
def kinetic_entropy(sc, mode, start_date, end_date):
# Read the data
b = fgm.load_data(sc, mode, start_date, end_date)
dis_moms = fpi.load_moms(sc, mode, 'i', start_date, end_date)
des_moms = fpi.load_moms(sc, mode, 'e', start_date, end_date)
dis_dist = fpi.load_dist(sc, mode, 'i', start_date, end_date)
des_dist = fpi.load_dist(sc, mode, 'e', start_date, end_date)
# Philosopy
# - For the Maxwellian distributions, use the FPI moments data
# whenever possible
# - For the integrated moments, do not mix them with FPI moments
# Equivalent Maxwellian distribution
dis_max_dist = fpi.maxwellian_distribution(dis_dist,
N=dis_moms['density'],
bulkv=dis_moms['velocity'],
T=dis_moms['t'])
des_max_dist = fpi.maxwellian_distribution(des_dist,
N=des_moms['density'],
bulkv=des_moms['velocity'],
T=des_moms['t'])
# Spacecraft potential correction
edp_mode = mode if mode == 'brst' else 'fast'
scpot = edp.load_scpot(sc, edp_mode, start_date, end_date)
scpot_dis = scpot.interp_like(dis_moms, method='nearest')
scpot_des = scpot.interp_like(des_moms, method='nearest')
# Maxwellian entropy density
# - Calculated from FPI moments to stick with philosophy
# si_dist = fpi.entropy(dis_dist, scpot=scpot_dis)
# se_dist = fpi.entropy(des_dist, scpot=scpot_des)
si_max = fpi.maxwellian_entropy(dis_moms['density'], dis_moms['p'])
se_max = fpi.maxwellian_entropy(des_moms['density'], des_moms['p'])
# Velocity space entropy density
siv_dist = fpi.vspace_entropy(dis_dist,
# N=dis_moms['density'],
# s=si_dist,
scpot=scpot_dis)
sev_dist = fpi.vspace_entropy(des_dist,
# N=des_moms['density'],
# s=se_dist,
scpot=scpot_des)
siv_max = fpi.vspace_entropy(dis_max_dist,
N=dis_moms['density'],
s=si_max,
scpot=scpot_dis)
sev_max = fpi.vspace_entropy(des_max_dist,
N=des_moms['density'],
s=se_max,
scpot=scpot_des)
# M-bar
miv_bar = np.abs(siv_max - siv_dist) / siv_max
mev_bar = np.abs(sev_max - sev_dist) / sev_max
# Anisotropy
Ai = dis_moms['temppara'] / dis_moms['tempperp'] - 1
Ae = des_moms['temppara'] / des_moms['tempperp'] - 1
nrows = 8
ncols = 1
figsize = (5.5, 7.0)
fig, axes = plt.subplots(nrows=nrows, ncols=ncols,
figsize=figsize, squeeze=False)
# B
ax = axes[0,0]
ax = util.plot([b['B'][:,3], b['B'][:,0], b['B'][:,1], b['B'][:,2]],
ax=ax, labels=['|B|', 'Bx', 'By', 'Bz'],
xaxis='off', ylabel='B\n(nT)'
)
# N
ax = axes[1,0]
ax = util.plot([dis_moms['density'], des_moms['density']],
ax=ax, labels=['Ni', 'Ne'],
xaxis='off', ylabel='N\n($cm^{-3}$)'
)
# Velocity space ion entropy density
ax = axes[2,0]
ax = util.plot([siv_max, siv_dist],
ax=ax, labels=['$s_{i,V,max}$', '$s_{i,V}$'],
xaxis='off', ylabel='$s_{V}$\n$J/K/m^{3}$ $ln()$'
)
# Velocity space electron entropy density
ax = axes[3,0]
ax = util.plot([sev_max, sev_dist],
ax=ax, labels=['$s_{e,V,max}$', '$s_{e,V}$'],
xaxis='off', ylabel='$s_{V}$'
)
# Velocity space M-bar
ax = axes[4,0]
ax = util.plot([miv_bar, mev_bar],
ax=ax, labels=['$\overline{M}_{i,V}$', '$\overline{M}_{e,V}$'],
xaxis='off', ylabel='$\overline{M}_{V}$'
)
# Ion temperature
ax = axes[5,0]
ax = util.plot([dis_moms['temppara'], dis_moms['tempperp'], dis_moms['t']],
ax=ax, labels=['$T_{\parallel}$', '$T_{\perp}$, $T$'],
xaxis='off', ylabel='$T_{i}$\n(eV)'
)
# Electron temperature
ax = axes[6,0]
ax = util.plot([des_moms['temppara'], des_moms['tempperp'], des_moms['t']],
ax=ax, labels=['$T_{\parallel}$', '$T_{\perp}$, $T$'],
xaxis='off', ylabel='$T_{e}$\n(eV)'
)
# Anisotropy
ax = axes[7,0]
ax = util.plot([Ai, Ae],
ax=ax, labels=['$A_{i}$', '$A_{e}$'],
ylabel='A'
)
fig.suptitle('Plasma Parameters for Kinetic and Boltzmann Entropy')
plt.subplots_adjust(left=0.2, right=0.85, top=0.95, hspace=0.4)
# plt.setp(axes, xlim=xlim)
return fig, axes
def vspace_entropy(sc, mode, start_date, end_date):
# Read the data
b = fgm.load_data(sc, mode, start_date, end_date)
dis_moms = fpi.load_moms(sc, mode, optdesc='dis-moms',
start_date=start_date, end_date=end_date)
des_moms = fpi.load_moms(sc, mode, optdesc='des-moms',
start_date=start_date, end_date=end_date)
dis_dist = fpi.load_dist(sc, mode, optdesc='dis-dist',
start_date=start_date, end_date=end_date)
des_dist = fpi.load_dist(sc, mode, optdesc='des-dist',
start_date=start_date, end_date=end_date)
# Precondition the distributions
dis_kwargs = fpi.precond_params(sc, mode, 'l2', 'dis-dist',
start_date, end_date,
time=dis_dist['time'])
des_kwargs = fpi.precond_params(sc, mode, 'l2', 'des-dist',
start_date, end_date,
time=des_dist['time'])
f_dis = fpi.precondition(dis_dist['dist'], **dis_kwargs)
f_des = fpi.precondition(des_dist['dist'], **des_kwargs)
# Calculate moments
# - Use calculated moments for the Maxwellian distribution
Ni = fpi.density(f_dis)
Vi = fpi.velocity(f_dis, N=Ni)
Ti = fpi.temperature(f_dis, N=Ni, V=Vi)
Pi = fpi.pressure(f_dis, N=Ni, T=Ti)
ti = ((Ti[:,0,0] + Ti[:,1,1] + Ti[:,2,2]) / 3.0).drop(['t_index_dim1', 't_index_dim2'])
pi = ((Pi[:,0,0] + Pi[:,1,1] + Pi[:,2,2]) / 3.0).drop(['t_index_dim1', 't_index_dim2'])
Ne = fpi.density(f_des)
Ve = fpi.velocity(f_des, N=Ne)
Te = fpi.temperature(f_des, N=Ne, V=Ve)
Pe = fpi.pressure(f_des, N=Ne, T=Te)
te = ((Te[:,0,0] + Te[:,1,1] + Te[:,2,2]) / 3.0).drop(['t_index_dim1', 't_index_dim2'])
pe = ((Pe[:,0,0] + Pe[:,1,1] + Pe[:,2,2]) / 3.0).drop(['t_index_dim1', 't_index_dim2'])
# Equivalent (preconditioned) Maxwellian distributions
# fi_max = fpi.maxwellian_distribution(f_dis, N=Ni, bulkv=Vi, T=ti)
# fe_max = fpi.maxwellian_distribution(f_des, N=Ne, bulkv=Ve, T=te)
fi_max = fpi.maxwellian_distribution(f_dis, N=Ni, bulkv=Vi, T=ti)
fe_max = fpi.maxwellian_distribution(f_des, N=Ne, bulkv=Ve, T=te)
# Analytically derived Maxwellian entropy density
# si_max = fpi.maxwellian_entropy(Ni, pi)
# se_max = fpi.maxwellian_entropy(Ne, pe)
si_max = fpi.entropy(fi_max)
se_max = fpi.entropy(fe_max)
# Velocity space entropy density
siv_dist = fpi.vspace_entropy(f_dis)
sev_dist = fpi.vspace_entropy(f_des)
# The Maxwellian is already preconditioned
# - There are three options for calculating the v-space entropy of
# the Maxwellian distribution: using
# 1) FPI integrated moments,
# 2) Custom moments of the measured distribution
# 3) Custom moments of the equivalent Maxwellian distribution
# Because the Maxwellian is built with discrete v-space bins, its
# density, velocity, and temperature do not match that of the
# measured distribution on which it is based. If NiM is used, the
# M-bar term will be negative, which is unphysical, so here we use
# the density of the measured distribution and the entropy of the
# equivalent Maxwellian.
siv_max = fpi.vspace_entropy(fi_max, N=Ni, s=si_max)
sev_max = fpi.vspace_entropy(fe_max, N=Ne, s=se_max)
# M-bar
miv_bar = (siv_max - siv_dist) / siv_max
mev_bar = (sev_max - sev_dist) / sev_max
# Anisotropy
Ai = dis_moms['temppara'] / dis_moms['tempperp'] - 1
Ae = des_moms['temppara'] / des_moms['tempperp'] - 1
nrows = 8
ncols = 1
figsize = (5.5, 7.0)
fig, axes = plt.subplots(nrows=nrows, ncols=ncols,
figsize=figsize, squeeze=False)
# B
ax = axes[0,0]
ax = util.plot([b['B_GSE'][:,3], b['B_GSE'][:,0],
b['B_GSE'][:,1], b['B_GSE'][:,2]],
ax=ax, labels=['|B|', 'Bx', 'By', 'Bz'],
xaxis='off', ylabel='B\n(nT)'
)
# N
ax = axes[1,0]
ax = util.plot([dis_moms['density'], des_moms['density']],
ax=ax, labels=['Ni', 'Ne'],
xaxis='off', ylabel='N\n($cm^{-3}$)'
)
# Velocity space ion entropy density
ax = axes[2,0]
ax = util.plot([siv_max, siv_dist],
ax=ax, labels=['$s_{i,V,max}$', '$s_{i,V}$'],
xaxis='off', ylabel='$s_{V}$\n$J/K/m^{3}$'
)
# Velocity space electron entropy density
ax = axes[3,0]
ax = util.plot([sev_max, sev_dist],
ax=ax, labels=['$s_{e,V,max}$', '$s_{e,V}$'],
xaxis='off', ylabel='$s_{V}$'
)
# Velocity space M-bar
ax = axes[4,0]
ax = util.plot([miv_bar, mev_bar],
ax=ax, labels=['$\overline{M}_{i,V}$', '$\overline{M}_{e,V}$'],
xaxis='off', ylabel='$\overline{M}_{V}$'
)
# Ion temperature
ax = axes[5,0]
ax = util.plot([dis_moms['temppara'], dis_moms['tempperp'], dis_moms['t']],
ax=ax, labels=['$T_{\parallel}$', '$T_{\perp}$', 'T'],
xaxis='off', ylabel='$T_{i}$\n(eV)'
)
# Electron temperature
ax = axes[6,0]
ax = util.plot([des_moms['temppara'], des_moms['tempperp'], des_moms['t']],
ax=ax, labels=['$T_{\parallel}$', '$T_{\perp}$', 'T'],
xaxis='off', ylabel='$T_{e}$\n(eV)'
)
# Anisotropy
ax = axes[7,0]
ax = util.plot([Ai, Ae],
ax=ax, labels=['$A_{i}$', '$A_{e}$'],
ylabel='A'
)
fig.suptitle('Velocity Space Entropy')
plt.subplots_adjust(left=0.2, right=0.85, top=0.95, hspace=0.4)
return fig, axes
def moments_comparison(sc,
mode,
species,
start_date,
end_date,
scpot_correction=False,
ephoto_correction=False,
elimits=False):
'''
Compare moments derived from the 3D velocity distribution functions
from three sources: official FPI moments, derived herein, and those
of an equivalent Maxwellian distribution derived herein.
Parameters
----------
sc : str
Spacecraft identifier. Choices are ('mms1', 'mms2', 'mms3', 'mms4')
mode : str
Data rate mode. Choices are ('fast', 'brst')
species : str
Particle species. Choices are ('i', 'e') for ions and electrons,
respectively
start_date, end_date : `datetime.datetime`
Start and end dates and times of the time interval
scpot_correction : bool
Apply spacecraft potential correction to the distribution functions.
ephoto : bool
Subtract photo-electrons. Applicable to DES data only.
elimits : bool
Set upper and lower energy limits of integration
Returns
-------
fig : `matplotlib.figure`
Figure in which graphics are displayed
ax : list
List of `matplotlib.pyplot.axes` objects
'''
# Read the data
moms_xr = fpi.load_moms(sc=sc,
mode=mode,
optdesc='d' + species + 's-moms',
start_date=start_date,
end_date=end_date)
dist_xr = fpi.load_dist(sc=sc,
mode=mode,
optdesc='d' + species + 's-dist',
start_date=start_date,
end_date=end_date,
ephoto=ephoto_correction)
# Precondition the distributions
kwargs = fpi.precond_params(sc,
mode,
'l2',
'dis-dist',
start_date,
end_date,
time=dist_xr['time'])
if scpot_correction is False:
kwargs['scpot'] == None
if elimits is False:
kwargs['E_low'] = None
kwargs['E_high'] = None
f = fpi.precondition(dist_xr['dist'], **kwargs)
# Moments distribution
n_xr = fpi.density(f)
s_xr = fpi.entropy(f)
v_xr = fpi.velocity(f, N=n_xr)
T_xr = fpi.temperature(f, N=n_xr, V=v_xr)
P_xr = fpi.pressure(f, N=n_xr, T=T_xr)
t_scalar_xr = (T_xr[:, 0, 0] + T_xr[:, 1, 1] + T_xr[:, 2, 2]) / 3.0
p_scalar_xr = (P_xr[:, 0, 0] + P_xr[:, 1, 1] + P_xr[:, 2, 2]) / 3.0
t_scalar_xr = t_scalar_xr.drop(['t_index_dim1', 't_index_dim2'])
p_scalar_xr = p_scalar_xr.drop(['t_index_dim1', 't_index_dim2'])
# Create an equivalent Maxwellian distribution
f_max_xr = fpi.maxwellian_distribution(f, n_xr, v_xr, t_scalar_xr)
n_max_dist = fpi.density(f_max_xr)
s_max_dist = fpi.entropy(f_max_xr)
s_max = fpi.maxwellian_entropy(n_xr, p_scalar_xr)
v_max_dist = fpi.velocity(f_max_xr, N=n_max_dist)
T_max_dist = fpi.temperature(f_max_xr, N=n_max_dist, V=v_max_dist)
P_max_dist = fpi.pressure(f_max_xr, N=n_max_dist, T=T_max_dist)
p_scalar_max_dist = (P_max_dist[:, 0, 0] + P_max_dist[:, 1, 1] +
P_max_dist[:, 2, 2]) / 3.0
p_scalar_max_dist = p_scalar_max_dist.drop(
['t_index_dim1', 't_index_dim2'])
# Epsilon
e_xr = fpi.epsilon(f, dist_max=f_max_xr, N=n_xr)
nrows = 6
ncols = 3
figsize = (10.0, 5.5)
fig, axes = plt.subplots(nrows=nrows,
ncols=ncols,
figsize=figsize,
squeeze=False)
# Density
ax = axes[0, 0]
moms_xr['density'].attrs['label'] = 'moms'
n_xr.attrs['label'] = 'dist'
n_max_dist.attrs['label'] = 'max'
ax = util.plot([moms_xr['density'], n_xr, n_max_dist],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='N\n($cm^{-3}$)')
# Entropy
ax = axes[1, 0]
ax = util.plot([s_max, s_xr, s_max_dist],
ax=ax,
labels=['moms', 'dist', 'max dist'],
xaxis='off',
ylabel='S\n[J/K/$m^{3}$ ln($s^{3}/m^{6}$)]')
# Epsilon
ax = axes[1, 0].twinx()
e_xr.plot(ax=ax, color='r')
ax.spines['right'].set_color('red')
ax.yaxis.label.set_color('red')
ax.tick_params(axis='y', colors='red')
ax.set_title('')
ax.set_xticks([])
ax.set_xlabel('')
ax.set_ylabel('$\epsilon$\n$(s/m)^{3/2}$')
# Vx
ax = axes[2, 0]
ax = util.plot([moms_xr['velocity'][:, 0], v_xr[:, 0], v_max_dist[:, 0]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Vx\n(km/s)')
# Vy
ax = axes[3, 0]
ax = util.plot([moms_xr['velocity'][:, 1], v_xr[:, 1], v_max_dist[:, 1]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Vy\n(km/s)')
# Vz
ax = axes[4, 0]
ax = util.plot([moms_xr['velocity'][:, 2], v_xr[:, 2], v_max_dist[:, 2]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Vz\n(km/s)')
# Scalar Pressure
ax = axes[5, 0]
ax = util.plot([moms_xr['p'], p_scalar_xr, p_scalar_max_dist],
ax=ax,
labels=['moms', 'dist', 'max'],
xlabel='',
ylabel='p\n(nPa)')
# T_xx
ax = axes[0, 1]
ax = util.plot(
[moms_xr['temptensor'][:, 0, 0], T_xr[:, 0, 0], T_max_dist[:, 0, 0]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Txx\n(eV)')
# T_yy
ax = axes[1, 1]
ax = util.plot(
[moms_xr['temptensor'][:, 1, 1], T_xr[:, 1, 1], T_max_dist[:, 1, 1]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Tyy\n(eV)')
# T_zz
ax = axes[2, 1]
ax = util.plot(
[moms_xr['temptensor'][:, 2, 2], T_xr[:, 2, 2], T_max_dist[:, 2, 2]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Tzz\n(eV)')
# T_xy
ax = axes[3, 1]
ax = util.plot(
[moms_xr['temptensor'][:, 0, 1], T_xr[:, 0, 1], T_max_dist[:, 0, 1]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Txy\n(eV)')
# T_xz
ax = axes[4, 1]
ax = util.plot(
[moms_xr['temptensor'][:, 0, 2], T_xr[:, 0, 2], T_max_dist[:, 0, 2]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Txz\n(eV)')
# T_yz
ax = axes[5, 1]
ax = util.plot(
[moms_xr['temptensor'][:, 1, 2], T_xr[:, 1, 2], T_max_dist[:, 1, 2]],
ax=ax,
labels=['moms', 'dist', 'max'],
xlabel='',
ylabel='Txz\n(eV)')
# P_xx
ax = axes[0, 2]
ax = util.plot(
[moms_xr['prestensor'][:, 0, 0], P_xr[:, 0, 0], P_max_dist[:, 0, 0]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Pxx\n(nPa)')
# P_yy
ax = axes[1, 2]
ax = util.plot(
[moms_xr['prestensor'][:, 1, 1], P_xr[:, 1, 1], P_max_dist[:, 1, 1]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Pyy\n(nPa)')
# P_zz
ax = axes[2, 2]
ax = util.plot(
[moms_xr['prestensor'][:, 2, 2], P_xr[:, 2, 2], P_max_dist[:, 2, 2]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Pzz\n(nPa)')
# P_xy
ax = axes[3, 2]
ax = util.plot(
[moms_xr['prestensor'][:, 0, 1], P_xr[:, 0, 1], P_max_dist[:, 0, 1]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Pxy\n(nPa)')
# P_xz
ax = axes[4, 2]
ax = util.plot(
[moms_xr['prestensor'][:, 0, 2], P_xr[:, 0, 2], P_max_dist[:, 0, 2]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Pxz\n(nPa)')
# P_yz
ax = axes[5, 2]
ax = util.plot(
[moms_xr['prestensor'][:, 1, 2], P_xr[:, 1, 2], P_max_dist[:, 1, 2]],
ax=ax,
labels=['moms', 'dist', 'max'],
xlabel='',
ylabel='Pyz\n(nPa)')
fig.suptitle(
'Comparing FPI Moments, Integrated Distribution, Equivalent Maxwellian'
)
plt.subplots_adjust(left=0.1,
right=0.90,
top=0.95,
bottom=0.12,
hspace=0.3,
wspace=0.8)
return fig, axes
def overview(sc, mode, start_date, end_date, **kwargs):
# Read the data
b = fgm.load_data(sc=sc, mode=mode,
start_date=start_date, end_date=end_date)
e = edp.load_data(sc=sc, mode=mode,
start_date=start_date, end_date=end_date)
dis_moms = fpi.load_moms(sc=sc, mode=mode, optdesc='dis-moms',
start_date=start_date, end_date=end_date)
des_moms = fpi.load_moms(sc=sc, mode=mode, optdesc='des-moms',
start_date=start_date, end_date=end_date)
nrows = 7
ncols = 1
figsize = (6.0, 7.0)
fig, axes = plt.subplots(nrows=nrows, ncols=ncols,
figsize=figsize, squeeze=False)
locator = mdates.AutoDateLocator()
formatter = mdates.ConciseDateFormatter(locator)
# B
ax = axes[0,0]
b['B_GSE'][:,3].plot(ax=ax, label='|B|')
b['B_GSE'][:,0].plot(ax=ax, label='Bx')
b['B_GSE'][:,1].plot(ax=ax, label='By')
b['B_GSE'][:,2].plot(ax=ax, label='Bz')
ax.set_xlabel('')
ax.set_xticklabels([])
ax.set_ylabel('B [nT]')
ax.set_title('')
# Create the legend outside the right-most axes
leg = ax.legend(bbox_to_anchor=(1.05, 1),
borderaxespad=0.0,
frameon=False,
handlelength=0,
handletextpad=0,
loc='upper left')
# Color the legend text the same color as the lines
for line, text in zip(ax.get_lines(), leg.get_texts()):
text.set_color(line.get_color())
# Ion energy spectrogram
nt = dis_moms['omnispectr'].shape[0]
nE = dis_moms['omnispectr'].shape[1]
x0 = mdates.date2num(dis_moms['time'])[:, np.newaxis].repeat(nE, axis=1)
x1 = dis_moms['energy']
y = np.where(dis_moms['omnispectr'] == 0, 1, dis_moms['omnispectr'])
y = np.log(y[0:-1,0:-1])
ax = axes[1,0]
im = ax.pcolorfast(x0, x1, y, cmap='nipy_spectral')
ax.images.append(im)
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(formatter)
ax.set_xticklabels([])
ax.set_xlabel('')
ax.set_yscale('log')
ax.set_ylabel('E ion\n(eV)')
cbaxes = inset_axes(ax,
width='2%', height='100%', loc=4,
bbox_to_anchor=(0, 0, 1.05, 1),
bbox_transform=ax.transAxes,
borderpad=0)
cb = plt.colorbar(im, cax=cbaxes, orientation='vertical')
cb.set_label('$log_{10}$DEF\nkeV/($cm^{2}$ s sr keV)')
# Electron energy spectrogram
nt = des_moms['omnispectr'].shape[0]
nE = des_moms['omnispectr'].shape[1]
x0 = mdates.date2num(des_moms['time'])[:, np.newaxis].repeat(nE, axis=1)
x1 = des_moms['energy']
y = np.where(des_moms['omnispectr'] == 0, 1, des_moms['omnispectr'])
y = np.log(y[0:-1,0:-1])
ax = axes[2,0]
im = ax.pcolorfast(x0, x1, y, cmap='nipy_spectral')
ax.images.append(im)
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(formatter)
ax.set_xticklabels([])
ax.set_xlabel('')
ax.set_yscale('log')
ax.set_ylabel('E e-\n(eV)')
cbaxes = inset_axes(ax,
width='2%', height='100%', loc=4,
bbox_to_anchor=(0, 0, 1.05, 1),
bbox_transform=ax.transAxes,
borderpad=0)
cb = plt.colorbar(im, cax=cbaxes, orientation='vertical')
cb.set_label('$log_{10}$DEF')
# N
ax = axes[3,0]
dis_moms['density'].plot(ax=ax, label='Ni')
des_moms['density'].plot(ax=ax, label='Ne')
ax.set_xlabel('')
ax.set_xticklabels([])
ax.set_ylabel('N\n($cm^{-3}$)')
ax.set_title('')
leg = ax.legend(bbox_to_anchor=(1.05, 1),
borderaxespad=0.0,
frameon=False,
handlelength=0,
handletextpad=0,
loc='upper left')
for line, text in zip(ax.get_lines(), leg.get_texts()):
text.set_color(line.get_color())
# Vi
ax = axes[4,0]
dis_moms['velocity'][:,0].plot(ax=ax, label='Vx')
dis_moms['velocity'][:,1].plot(ax=ax, label='Vy')
dis_moms['velocity'][:,2].plot(ax=ax, label='Vz')
ax.set_xlabel('')
ax.set_xticklabels([])
ax.set_ylabel('Vi\n(km/s)')
ax.set_title('')
leg = ax.legend(bbox_to_anchor=(1.05, 1),
borderaxespad=0.0,
frameon=False,
handlelength=0,
handletextpad=0,
loc='upper left')
for line, text in zip(ax.get_lines(), leg.get_texts()):
text.set_color(line.get_color())
# Ve
ax = axes[5,0]
des_moms['velocity'][:,0].plot(ax=ax, label='Vx')
des_moms['velocity'][:,1].plot(ax=ax, label='Vy')
des_moms['velocity'][:,2].plot(ax=ax, label='Vz')
ax.set_xlabel('')
ax.set_xticklabels([])
ax.set_ylabel('Ve\n(km/s)')
ax.set_title('')
leg = ax.legend(bbox_to_anchor=(1.05, 1),
borderaxespad=0.0,
frameon=False,
handlelength=0,
handletextpad=0,
loc='upper left')
for line, text in zip(ax.get_lines(), leg.get_texts()):
text.set_color(line.get_color())
# E
ax = axes[6,0]
e['E_GSE'][:,0].plot(ax=ax, label='Ex')
e['E_GSE'][:,1].plot(ax=ax, label='Ey')
e['E_GSE'][:,2].plot(ax=ax, label='Ez')
ax.set_xlabel('')
ax.set_xticklabels([])
ax.set_ylabel('E\n(mV/m)')
ax.set_title('')
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(formatter)
for tick in ax.get_xticklabels():
tick.set_rotation(45)
leg = ax.legend(bbox_to_anchor=(1.05, 1),
borderaxespad=0.0,
frameon=False,
handlelength=0,
handletextpad=0,
loc='upper left')
for line, text in zip(ax.get_lines(), leg.get_texts()):
text.set_color(line.get_color())
fig.suptitle(sc.upper())
plt.subplots_adjust(left=0.2, right=0.8, top=0.95, hspace=0.2)
return fig, axes
def compare_moments(sc,
mode,
species,
start_date,
end_date,
scpot_correction=False,
ephoto_correction=False):
'''
Compare moments derived from the 3D velocity distribution functions
from three sources: official FPI moments, derived herein, and those
of an equivalent Maxwellian distribution derived herein.
Parameters
----------
sc : str
Spacecraft identifier. Choices are ('mms1', 'mms2', 'mms3', 'mms4')
mode : str
Data rate mode. Choices are ('fast', 'brst')
species : str
Particle species. Choices are ('i', 'e') for ions and electrons,
respectively
start_date, end_date : `datetime.datetime`
Start and end dates and times of the time interval
scpot_correction : bool
Apply spacecraft potential correction to the distribution functions.
ephoto : bool
Subtract photo-electrons. Applicable to DES data only.
Returns
-------
fig : `matplotlib.figure`
Figure in which graphics are displayed
ax : list
List of `matplotlib.pyplot.axes` objects
'''
# Read the data
moms_xr = fpi.load_moms(sc, mode, species, start_date, end_date)
dist_xr = fpi.load_dist(sc,
mode,
species,
start_date,
end_date,
ephoto=ephoto_correction)
# Spacecraft potential correction
scpot = None
if scpot_correction:
edp_mode = mode if mode == 'brst' else 'fast'
scpot = edp.load_scpot(sc, edp_mode, start_date, end_date)
scpot = scpot.interp_like(moms_xr, method='nearest')
# Create an equivalent Maxwellian distribution
max_xr = fpi.maxwellian_distribution(dist_xr, moms_xr['density'],
moms_xr['velocity'], moms_xr['t'])
# Density
ni_xr = fpi.density(dist_xr, scpot=scpot)
ni_max_dist = fpi.density(max_xr, scpot=scpot)
# Entropy
s_xr = fpi.entropy(dist_xr, scpot=scpot)
s_max_dist = fpi.entropy(max_xr, scpot=scpot)
s_max = fpi.maxwellian_entropy(moms_xr['density'], moms_xr['p'])
# Velocity
v_xr = fpi.velocity(dist_xr, N=ni_xr, scpot=scpot)
v_max_dist = fpi.velocity(max_xr, N=ni_max_dist, scpot=scpot)
# Temperature
T_xr = fpi.temperature(dist_xr, N=ni_xr, V=v_xr, scpot=scpot)
T_max_dist = fpi.temperature(max_xr,
N=ni_max_dist,
V=v_max_dist,
scpot=scpot)
# Pressure
P_xr = fpi.pressure(dist_xr, N=ni_xr, T=T_xr)
P_max_dist = fpi.pressure(max_xr, N=ni_max_dist, T=T_max_dist)
# Scalar pressure
p_scalar_xr = (P_xr[:, 0, 0] + P_xr[:, 1, 1] + P_xr[:, 2, 2]) / 3.0
p_scalar_max_dist = (P_max_dist[:, 0, 0] + P_max_dist[:, 1, 1] +
P_max_dist[:, 2, 2]) / 3.0
p_scalar_xr = p_scalar_xr.drop(['t_index_dim1', 't_index_dim2'])
p_scalar_max_dist = p_scalar_max_dist.drop(
['t_index_dim1', 't_index_dim2'])
# Epsilon
e_xr = fpi.epsilon(dist_xr, dist_max=max_xr, N=ni_xr)
nrows = 6
ncols = 3
figsize = (10.0, 5.5)
fig, axes = plt.subplots(nrows=nrows,
ncols=ncols,
figsize=figsize,
squeeze=False)
'''
locator = mdates.AutoDateLocator()
formatter = mdates.ConciseDateFormatter(locator)
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(formatter)
for tick in ax.get_xticklabels():
tick.set_rotation(45)
# Denisty
ax = axes[0,0]
lines = []
moms_xr['density'].plot(ax=ax, label='moms')
ni_xr.plot(ax=ax, label='dist')
ni_max_dist.plot(ax=ax, label='max')
ax.set_xlabel('')
ax.set_xticklabels([])
ax.set_ylabel='N\n($cm^{-3}$)'
# Create the legend outside the right-most axes
leg = ax.legend(bbox_to_anchor=(1.05, 1),
borderaxespad=0.0,
frameon=False,
handlelength=0,
handletextpad=0,
loc='upper left')
# Color the text the same as the lines
for line, text in zip(lines, leg.get_texts()):
text.set_color(line.get_color())
'''
# Density
ax = axes[0, 0]
moms_xr['density'].attrs['label'] = 'moms'
ni_xr.attrs['label'] = 'dist'
ni_max_dist.attrs['label'] = 'max'
ax = util.plot([moms_xr['density'], ni_xr, ni_max_dist],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='N\n($cm^{-3}$)')
# Entropy
ax = axes[1, 0]
ax = util.plot([s_max, s_xr, s_max_dist],
ax=ax,
labels=['moms', 'dist', 'max dist'],
xaxis='off',
ylabel='S\n[J/K/$m^{3}$ ln($s^{3}/m^{6}$)]')
# Epsilon
ax = axes[1, 0].twinx()
e_xr.plot(ax=ax, color='r')
ax.spines['right'].set_color('red')
ax.yaxis.label.set_color('red')
ax.tick_params(axis='y', colors='red')
ax.set_title('')
ax.set_xticks([])
ax.set_xlabel('')
ax.set_ylabel('$\epsilon$\n$(s/m)^{3/2}$')
# Vx
ax = axes[2, 0]
ax = util.plot([moms_xr['velocity'][:, 0], v_xr[:, 0], v_max_dist[:, 0]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Vx\n(km/s)')
# Vy
ax = axes[3, 0]
ax = util.plot([moms_xr['velocity'][:, 1], v_xr[:, 1], v_max_dist[:, 1]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Vy\n(km/s)')
# Vz
ax = axes[4, 0]
ax = util.plot([moms_xr['velocity'][:, 2], v_xr[:, 2], v_max_dist[:, 2]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Vz\n(km/s)')
# Scalar Pressure
ax = axes[5, 0]
ax = util.plot([moms_xr['p'], p_scalar_xr, p_scalar_max_dist],
ax=ax,
labels=['moms', 'dist', 'max'],
xlabel='',
ylabel='p\n(nPa)')
# T_xx
ax = axes[0, 1]
ax = util.plot(
[moms_xr['temptensor'][:, 0, 0], T_xr[:, 0, 0], T_max_dist[:, 0, 0]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Txx\n(eV)')
# T_yy
ax = axes[1, 1]
ax = util.plot(
[moms_xr['temptensor'][:, 1, 1], T_xr[:, 1, 1], T_max_dist[:, 1, 1]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Tyy\n(eV)')
# T_zz
ax = axes[2, 1]
ax = util.plot(
[moms_xr['temptensor'][:, 2, 2], T_xr[:, 2, 2], T_max_dist[:, 2, 2]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Tzz\n(eV)')
# T_xy
ax = axes[3, 1]
ax = util.plot(
[moms_xr['temptensor'][:, 0, 1], T_xr[:, 0, 1], T_max_dist[:, 0, 1]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Txy\n(eV)')
# T_xz
ax = axes[4, 1]
ax = util.plot(
[moms_xr['temptensor'][:, 0, 2], T_xr[:, 0, 2], T_max_dist[:, 0, 2]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Txz\n(eV)')
# T_yz
ax = axes[5, 1]
ax = util.plot(
[moms_xr['temptensor'][:, 1, 2], T_xr[:, 1, 2], T_max_dist[:, 1, 2]],
ax=ax,
labels=['moms', 'dist', 'max'],
xlabel='',
ylabel='Txz\n(eV)')
# P_xx
ax = axes[0, 2]
ax = util.plot(
[moms_xr['prestensor'][:, 0, 0], P_xr[:, 0, 0], P_max_dist[:, 0, 0]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Pxx\n(nPa)')
# P_yy
ax = axes[1, 2]
ax = util.plot(
[moms_xr['prestensor'][:, 1, 1], P_xr[:, 1, 1], P_max_dist[:, 1, 1]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Pyy\n(nPa)')
# P_zz
ax = axes[2, 2]
ax = util.plot(
[moms_xr['prestensor'][:, 2, 2], P_xr[:, 2, 2], P_max_dist[:, 2, 2]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Pzz\n(nPa)')
# P_xy
ax = axes[3, 2]
ax = util.plot(
[moms_xr['prestensor'][:, 0, 1], P_xr[:, 0, 1], P_max_dist[:, 0, 1]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Pxy\n(nPa)')
# P_xz
ax = axes[4, 2]
ax = util.plot(
[moms_xr['prestensor'][:, 0, 2], P_xr[:, 0, 2], P_max_dist[:, 0, 2]],
ax=ax,
labels=['moms', 'dist', 'max'],
xaxis='off',
ylabel='Pxz\n(nPa)')
# P_yz
ax = axes[5, 2]
ax = util.plot(
[moms_xr['prestensor'][:, 1, 2], P_xr[:, 1, 2], P_max_dist[:, 1, 2]],
ax=ax,
labels=['moms', 'dist', 'max'],
xlabel='',
ylabel='Pyz\n(nPa)')
fig.suptitle(
'Comparing FPI Moments, Integrated Distribution, Equivalent Maxwellian'
)
plt.subplots_adjust(left=0.1,
right=0.90,
top=0.95,
bottom=0.12,
hspace=0.3,
wspace=0.8)
return fig, axes
def plot_photocurrent():
# Download the data
scpot = edp.load_scpot(sc=sc,
mode=mode,
start_date=orbit_t0,
end_date=orbit_t1)
dis_moms = fpi.load_moms(sc=sc,
mode=mode,
optdesc='dis-moms',
start_date=orbit_t0,
end_date=orbit_t1)
des_moms = fpi.load_moms(sc=sc,
mode=mode,
optdesc='des-moms',
start_date=orbit_t0,
end_date=orbit_t1)
aspoc = mms_util.load_data(sc=sc,
instr='aspoc',
mode='srvy',
level='l2',
start_date=orbit_t0,
end_date=orbit_t1)
# Bin data into FPI time stamps
t_bins = np.append(
dis_moms['time'].data - dis_moms['Epoch_minus_var'].data,
dis_moms['time'].data[-1] + dis_moms['Epoch_plus_var'].data)
Vsc, edges, binnum = binned_statistic(scpot['time'].astype('i8'),
scpot['Vsc'],
statistic='mean',
bins=t_bins.astype('i8'))
asp1, edges, binnum = binned_statistic(aspoc['Epoch'].astype('i8'),
aspoc[sc + '_asp1_ionc'],
statistic='mean',
bins=t_bins.astype('i8'))
idx = 0
data_bin = []
asp2 = np.empty_like(asp1)
asp_on = np.zeros(asp1.shape, dtype='?')
ref_idx = binnum[0]
for aspoc_idx, bin_idx in enumerate(binnum):
if (bin_idx == 0) | (bin_idx == len(aspoc[sc + '_asp1_ionc'])):
continue
elif ref_idx == 0:
ref_idx = bin_idx
if bin_idx == ref_idx:
data_bin.append(aspoc_idx)
else:
asp2[idx] = aspoc[sc + '_asp2_ionc'][data_bin].mean()
asp_on[idx] = aspoc[sc + '_aspoc_status'][data_bin, 3].max() > 0
idx += 1
ref_idx = bin_idx
data_bin = [aspoc_idx]
Vsc = xr.DataArray(Vsc, dims='time', coords={'time': dis_moms['time']})
asp = xr.Dataset(
{
'asp1': (['time'], asp1),
'asp2': (['time'], asp2),
'asp': (['time'], asp1 + asp2)
},
coords={'time': dis_moms['time']})
# Electron current
Ie = electron_current(des_moms['density'], des_moms['t'], Vsc)
# Flag the data
flag = xr.DataArray(asp_on.astype('int'),
dims='time',
coords={'time': dis_moms['time']})
flag += (((Ie < 1e-11) & (Vsc > 8) & (Vsc < 11)) * 2**1)
flag += (((Vsc > 14.75) & (Ie > 7.9e-12) & (Ie < 2e-11)) * 2**2)
flag += (((Vsc > 12.6) & (Vsc <= 14.75) & (Ie > 9.2e-12) & (Ie < 2e-11)) *
2**2)
#
# Fit the data
#
# Ie = Iph0 exp(-Vsc/Vph0)
# y = a exp(b * x)
# log(y) = log(a) + b*x
b, a = np.polyfit(Vsc[flag == 0],
np.log(Ie[flag == 0]),
1,
w=np.sqrt(Ie[flag == 0]))
Ie_fit = np.exp(a) * np.exp(b * Vsc[flag == 0])
str_fit = ('Ie = {0:0.3e} exp(-Vsc/{1:0.3e})'.format(
np.exp(a), 1 / np.exp(b)))
# Fit density to the spacecraft potential
c, b, a = np.polyfit(Vsc[flag == 0], des_moms['density'][flag == 0], 2)
Ne_fit2 = c * Vsc[flag == 0]**2 + b * Vsc[flag == 0] + a
str_Ne_fit2 = ('Ne = {0:0.3e}Vsc^2 + {1:0.3e}Vsc^1 + {2:0.3e})'.format(
c, b, a))
e, d, c, b, a = np.polyfit(Vsc[flag == 0], des_moms['density'][flag == 0],
4)
Ne_fit4 = e * Vsc[flag == 0]**4 + d * Vsc[flag == 0]**3 + c * Vsc[
flag == 0]**2 + b * Vsc[flag == 0] + a
str_Ne_fit4 = (
'Ne = {0:0.3e}Vsc^4 + {1:0.3e}Vsc^3 + {2:0.3e}Vsc^2 + {3:0.3e}Vsc + {4:0.3e})'
.format(e, d, c, b, a))
# Fit density to the inverse of the spacecraft potential
c, b, a = np.polyfit(1 / Vsc[flag == 0], des_moms['density'][flag == 0], 2)
invV_fit2 = c / Vsc[flag == 0]**2 + b / Vsc[flag == 0] + a
str_invV_fit2 = ('Ne = {0:0.3e}/Vsc^2 + {1:0.3e}/Vsc + {2:0.3e})'.format(
c, b, a))
e, d, c, b, a = np.polyfit(1 / Vsc[flag == 0],
des_moms['density'][flag == 0], 4)
invV_fit4 = e / Vsc[flag == 0]**4 + d / Vsc[flag == 0]**3 + c / Vsc[
flag == 0]**2 + b / Vsc[flag == 0] + a
str_invV_fit4 = (
'Ne = {0:0.3e}/Vsc^4 + {1:0.3e}/Vsc^3 + {2:0.3e}/Vsc^2 + {3:0.3e}/Vsc + {4:0.3e})'
.format(e, d, c, b, a))
#
# Plot: Time Series
#
# Plot current-related data
fig1, axes1 = plt.subplots(nrows=8,
ncols=1,
sharex=True,
figsize=(5, 6.5),
squeeze=False)
plt.subplots_adjust(left=0.17, right=0.85, bottom=0.14, top=0.95)
# Ion energy spectrogram
nt = dis_moms['omnispectr'].shape[0]
nE = dis_moms['omnispectr'].shape[1]
x0 = mdates.date2num(dis_moms['time'])[:, np.newaxis].repeat(nE, axis=1)
x1 = dis_moms['energy']
y = np.where(dis_moms['omnispectr'] == 0, 1, dis_moms['omnispectr'])
y = np.log(y[0:-1, 0:-1])
ax = axes1[0, 0]
im = ax.pcolorfast(x0, x1, y, cmap='nipy_spectral')
ax.images.append(im)
ax.set_title(sc.upper())
ax.set_yscale('log')
ax.set_ylabel('E (ion)\n(eV)')
util.format_axes(ax, xaxis='off')
cb = util.add_colorbar(ax, im)
cb.set_label('$log_{10}$DEF\nkeV/($cm^{2}$ s sr keV)')
# Electron energy spectrogram
nt = des_moms['omnispectr'].shape[0]
nE = des_moms['omnispectr'].shape[1]
x0 = mdates.date2num(des_moms['time'])[:, np.newaxis].repeat(nE, axis=1)
x1 = des_moms['energy']
y = np.where(des_moms['omnispectr'] == 0, 1, des_moms['omnispectr'])
y = np.log(y[0:-1, 0:-1])
ax = axes1[1, 0]
im = ax.pcolorfast(x0, x1, y, cmap='nipy_spectral')
ax.images.append(im)
ax.set_yscale('log')
ax.set_ylabel('E (e-)\n(eV)')
util.format_axes(ax, xaxis='off')
cb = util.add_colorbar(ax, im)
cb.set_label('$log_{10}$DEF\nkeV/($cm^{2}$ s sr keV)')
# Density
ax = axes1[2, 0]
l1 = dis_moms['density'].plot(ax=ax, label='$N_{i}$')
l2 = des_moms['density'].plot(ax=ax, label='$N_{e}$')
ax.set_title('')
ax.set_ylabel('N\n($cm^{3}$)')
ax.set_yscale('log')
util.format_axes(ax, xaxis='off')
util.add_legend(ax, [l1[0], l2[0]])
# Temperature
ax = axes1[3, 0]
l1 = dis_moms['t'].plot(ax=ax, label='$T_{i}$')
l2 = des_moms['t'].plot(ax=ax, label='$T_{e}$')
util.format_axes(ax, xaxis='off')
util.add_legend(ax, [l1[0], l2[0]])
ax.set_title('')
ax.set_ylabel('T\n(eV)')
ax.set_yscale('log')
# Spacecraft potential
ax = axes1[4, 0]
Vsc[~asp_on].plot(ax=ax)
Vsc[asp_on].plot(ax=ax, color='red')
util.format_axes(ax, xaxis='off')
ax.set_title('')
ax.set_ylabel('$V_{S/C}$\n(V)')
# Electron current
ax = axes1[5, 0]
Ie.plot(ax=ax)
ax.set_title('')
ax.set_ylabel('$I_{e}$\n($\mu A$)')
ax.set_yscale('log')
ax.set_ylim([1e-12, 1e-10])
util.format_axes(ax, xaxis='off')
# Aspoc Status
ax = axes1[6, 0]
l1 = asp['asp1'].plot(ax=ax, label='$ASP_{1}$')
l2 = asp['asp2'].plot(ax=ax, label='$ASP_{2}$')
l3 = asp['asp'].plot(ax=ax, label='$ASP_{tot}$')
util.format_axes(ax, xaxis='off')
util.add_legend(ax, [l1[0], l2[0], l3[0]])
ax.set_title('')
ax.set_xlabel('')
ax.set_ylabel('$I_{ASPOC}$\n($\mu A$)')
# Flag
ax = axes1[7, 0]
flag.plot(ax=ax)
util.format_axes(ax)
ax.set_title('')
ax.set_xlabel('')
ax.set_ylabel('Flag')
#
# Plot: Photocurrent Fit
#
# Plot the data
fig2, axes = plt.subplots(nrows=3,
ncols=1,
figsize=[5, 5.5],
squeeze=False)
plt.subplots_adjust(left=0.15, right=0.95, top=0.94)
# I_ph = I_ph0 exp(Vsc/Vph0)
ax = axes[0, 0]
ax.scatter(Vsc[flag == 0], Ie[flag == 0], marker='o')
ax.scatter(Vsc[np.bitwise_and(flag, 2**0) > 0],
Ie[np.bitwise_and(flag, 2**0) > 0],
marker='o',
color='red')
ax.scatter(Vsc[np.bitwise_and(flag, 2**1) > 0],
Ie[np.bitwise_and(flag, 2**1) > 0],
marker='o',
color='purple')
ax.scatter(Vsc[np.bitwise_and(flag, 2**2) > 0],
Ie[np.bitwise_and(flag, 2**2) > 0],
marker='o',
color='green')
ax.plot(Vsc[flag == 0], Ie_fit, color='black')
ax.set_title(
'Photocurrent Parameters $I_{e} = -I_{ph0} \exp(V_{S/C}/V_{ph0})$')
ax.set_xlabel('$V_{S/C}$ (V)')
ax.set_ylabel('$I_{e}$\n($\mu A$)')
ax.set_yscale('log')
ax.set_ylim([
10**np.floor(np.log10(Ie.min().values)),
10**np.ceil(np.log10(Ie.max().values))
])
ax.text(0.9 * ax.get_xlim()[1],
0.7 * ax.get_ylim()[1],
str_fit,
horizontalalignment='right',
verticalalignment='bottom',
color='black')
# Ne = \Sum a_i * V^i
ax = axes[1, 0]
ax.scatter(Vsc[flag == 0], des_moms['density'][flag == 0], marker='o')
ax.scatter(Vsc[np.bitwise_and(flag, 2**0) > 0],
des_moms['density'][np.bitwise_and(flag, 2**0) > 0],
marker='o',
color='red')
ax.scatter(Vsc[np.bitwise_and(flag, 2**1) > 0],
des_moms['density'][np.bitwise_and(flag, 2**1) > 0],
marker='o',
color='purple')
ax.scatter(Vsc[np.bitwise_and(flag, 2**2) > 0],
des_moms['density'][np.bitwise_and(flag, 2**2) > 0],
marker='o',
color='green')
ax.plot(Vsc[flag == 0], Ne_fit2, color='black')
ax.plot(Vsc[flag == 0], Ne_fit4, color='grey')
ax.set_title('')
ax.set_xlabel('$V_{S/C}$ (V)')
ax.set_ylabel('$N_{e}$\n($cm^{-3}$)')
ax.text(0.98 * ax.get_xlim()[1],
0.85 * ax.get_ylim()[1],
str_Ne_fit2,
horizontalalignment='right',
verticalalignment='bottom',
color='black')
ax.text(0.98 * ax.get_xlim()[1],
0.75 * ax.get_ylim()[1],
str_Ne_fit4,
horizontalalignment='right',
verticalalignment='bottom',
color='grey')
# Ne = \Sum a_i * V^-i
ax = axes[2, 0]
ax.scatter(1 / Vsc[flag == 0], des_moms['density'][flag == 0], marker='o')
ax.scatter(1 / Vsc[np.bitwise_and(flag, 2**0) > 0],
des_moms['density'][np.bitwise_and(flag, 2**0) > 0],
marker='o',
color='red')
ax.scatter(1 / Vsc[np.bitwise_and(flag, 2**1) > 0],
des_moms['density'][np.bitwise_and(flag, 2**1) > 0],
marker='o',
color='purple')
ax.scatter(1 / Vsc[np.bitwise_and(flag, 2**2) > 0],
des_moms['density'][np.bitwise_and(flag, 2**2) > 0],
marker='o',
color='green')
ax.plot(Vsc[flag == 0], invV_fit2, color='black')
ax.plot(Vsc[flag == 0], invV_fit4, color='grey')
ax.set_title('')
ax.set_xlabel('1/$V_{S/C}$ ($V^{-1}$)')
ax.set_ylabel('$N_{e}$\n($cm^{-3}$)')
ax.text(0.98 * ax.get_xlim()[1],
0.85 * ax.get_ylim()[1],
str_invV_fit2,
horizontalalignment='right',
verticalalignment='bottom',
color='black')
ax.text(0.98 * ax.get_xlim()[1],
0.75 * ax.get_ylim()[1],
str_invV_fit4,
horizontalalignment='right',
verticalalignment='bottom',
color='grey')
return [fig1, fig2], ax