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 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 kinetic_entropy(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)
dis_dist = fpi.load_dist(sc=sc, mode=mode, optdesc='dis-dist',
start_date=start_date, end_date=end_date)
des_dist = fpi.load_dist(sc=sc, mode=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
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)
# Entropy density
si_dist = fpi.entropy(f_dis)
se_dist = fpi.entropy(f_des)
si_max = fpi.entropy(fi_max)
se_max = fpi.entropy(fe_max)
# Velcoity space entropy density
siv_dist = fpi.vspace_entropy(f_dis, N=Ni, s=si_dist)
sev_dist = fpi.vspace_entropy(f_des, N=Ne, s=se_dist)
# 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
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(f_dis, N=Ni, V=Vi, T=ti)
ee = fpi.epsilon(f_des, N=Ne, V=Ve, T=te)
# 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_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)'
)
# 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([])
ax.set_title('')
# 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([])
ax.set_title('')
# 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 maxwellian_lookup_table(sc,
mode,
species,
start_date,
end_date,
lut_file=None,
minimize='both'):
instr = 'fpi'
level = 'l2'
optdesc = 'd' + species + 's-dist'
# Name of the look-up table
if lut_file is None:
lut_file = data_root / '_'.join(
(sc, instr, mode, level, optdesc + 'lookup-table',
start_date.strftime('%Y%m%d_%H%M%S'),
end_date.strftime('%Y%m%d_%H%M%S')))
lut_file = lut_file.with_suffix('.ncdf')
# Ensure it is Path-like
else:
lut_file = Path(lut_file).expanduser().absolute()
# Read the data
fpi_dist = fpi.load_dist(sc=sc,
mode=mode,
optdesc=optdesc,
start_date=start_date,
end_date=end_date)
# Precondition the distributions
fpi_kwargs = fpi.precond_params(sc,
mode,
level,
optdesc,
start_date,
end_date,
time=fpi_dist['time'])
f = fpi.precondition(fpi_dist['dist'], **fpi_kwargs)
# Calculate Moments
N = fpi.density(f)
V = fpi.velocity(f, N=N)
T = fpi.temperature(f, N=N, V=V)
P = fpi.pressure(f, N=N, T=T)
s = fpi.entropy(f)
sv = fpi.vspace_entropy(f, N=N, s=s)
t = ((T[:, 0, 0] + T[:, 1, 1] + T[:, 2, 2]) / 3.0).drop(
['t_index_dim1', 't_index_dim2'])
p = ((P[:, 0, 0] + P[:, 1, 1] + P[:, 2, 2]) / 3.0).drop(
['t_index_dim1', 't_index_dim2'])
s_max_moms = fpi.maxwellian_entropy(N, p)
# Create equivalent Maxwellian distributions and calculate moments
f_max = fpi.maxwellian_distribution(f, N=N, bulkv=V, T=t)
N_max = fpi.density(f_max)
V_max = fpi.velocity(f_max, N=N_max)
T_max = fpi.temperature(f_max, N=N_max, V=V_max)
P_max = fpi.pressure(f_max, N=N_max, T=T_max)
s_max = fpi.entropy(f_max)
sv_max = fpi.vspace_entropy(f, N=N_max, s=s_max)
t_max = ((T_max[:, 0, 0] + T_max[:, 1, 1] + T_max[:, 2, 2]) / 3.0).drop(
['t_index_dim1', 't_index_dim2'])
p_max = ((P_max[:, 0, 0] + P_max[:, 1, 1] + P_max[:, 2, 2]) / 3.0).drop(
['t_index_dim1', 't_index_dim2'])
# Create the lookup table of Maxwellian distributions if it does not exist
if not lut_file.exists():
N_range = (0.9 * N.min().values, 1.1 * N.max().values)
t_range = (0.9 * t.min().values, 1.1 * t.max().values)
dims = (int(10**max(
np.floor(np.abs(np.log10(N_range[1] - N_range[0]))), 1)),
int(10**max(
np.floor(np.abs(np.log10(t_range[1] - t_range[0]))), 1)))
fpi.maxwellian_lookup(f[[0], ...],
N_range,
t_range,
dims=dims,
fname=lut_file)
# Read the dataset
lut = xr.load_dataset(lut_file)
dims = lut['N'].shape
# Find the error in N and T between the Maxwellian and Measured
# distribution
N_grid, t_grid = np.meshgrid(lut['N_data'], lut['t_data'], indexing='ij')
dN = (N_grid - lut['N']) / N_grid * 100.0
dt = (t_grid - lut['t']) / t_grid * 100.0
N_lut = xr.zeros_like(N)
t_lut = xr.zeros_like(t)
s_lut = xr.zeros_like(s)
sv_lut = xr.zeros_like(sv)
if minimize == 'N':
for idx, dens in enumerate(N):
imin = np.argmin(np.abs(lut['N'].data - dens.item()))
irow = imin // dims[1]
icol = imin % dims[1]
N_lut[idx] = lut['N'][irow, icol]
t_lut[idx] = lut['t'][irow, icol]
s_lut[idx] = lut['s'][irow, icol]
sv_lut[idx] = lut['sv'][irow, icol]
elif minimize == 't':
for idx, temp in enumerate(t):
imin = np.argmin(np.abs(lut['t'].data - temp.item()))
irow = imin // dims[1]
icol = imin % dims[1]
N_lut[idx] = lut['N'][irow, icol]
t_lut[idx] = lut['t'][irow, icol]
s_lut[idx] = lut['s'][irow, icol]
sv_lut[idx] = lut['sv'][irow, icol]
elif minimize == 'both':
for idx, (dens, temp) in enumerate(zip(N, t)):
imin = np.argmin(
np.sqrt((lut['t'].data - temp.item())**2 +
(lut['N'].data - dens.item())**2))
irow = imin // dims[1]
icol = imin % dims[1]
N_lut[idx] = lut['N'][irow, icol]
t_lut[idx] = lut['t'][irow, icol]
s_lut[idx] = lut['s'][irow, icol]
sv_lut[idx] = lut['sv'][irow, icol]
# Create a time-series dataset of Maxwellian data by interpolating the
# lookup-table onto the timestamps of the data
elif minimize == 'data':
lut_interp = lut.interp({'N_data': N, 't_data': t}, method='linear')
N_lut = lut_interp['N']
t_lut = lut_interp['t']
s_lut = lut_interp['s']
sv_lut = lut_interp['sv']
# Create the figure
fig = plt.figure(figsize=(6, 7.5))
# Figure positions
# - Start with the position of a plot in the first row of a one-column
# set of plots.
# - Define the width and height spacing between each row
# - The first row will actually be broken into two columns with a gap
# between the first and second rows to allow for axis labels
# - Subsequent plots will be offset from the first by multiples of
# height and hspace
left, bottom, width, height = 0.15, 0.8, 0.7, 0.14
wspace, hspace, gap = 0.2, 0.02, 0.07
row1_width = 0.26
# Error in the look-up table density
# - Error is independent of density, so plot as 1D line plot
ax = fig.add_subplot(521)
dN[0, :].plot(ax=ax)
ax.set_title('')
ax.set_xlabel('$T_{' + species + '}$ (eV)')
ax.set_ylabel('$\Delta n_{' + species + '}/n_{' + species + '}$ (%)')
ax.set_position((left, bottom, row1_width, height))
util.format_axes(ax, time=False)
'''
img = dN.T.plot(ax=ax, cmap=cm.get_cmap('rainbow', 10), add_colorbar=False)
ax.set_title('')
ax.set_xlabel('$n_{'+species+'}$ ($cm^{-3}$)')
ax.set_ylabel('$T_{'+species+'}$ (eV)')
# Create a colorbar that is aware of the image's new position
divider = make_axes_locatable(ax)
cax = divider.new_horizontal(size="5%", pad=0.1)
fig.add_axes(cax)
cb = fig.colorbar(img, cax=cax, orientation="vertical")
cb.set_label('$\Delta n_{'+species+'}/n_{'+species+'}$ (%)')
cb.ax.minorticks_on()
'''
# Error in the look-up table temperature
# - Error is independent of density, so plot as 1D line plot
ax = fig.add_subplot(522)
dt[0, :].plot(ax=ax)
ax.set_title('')
ax.set_xlabel('$T_{' + species + '}$ (eV)')
ax.set_ylabel('$\Delta T_{' + species + '}/T_{' + species + '}$ (%)')
ax.set_position((left + row1_width + wspace, bottom, row1_width, height))
util.format_axes(ax, time=False)
'''
img = dt.T.plot(ax=ax, cmap=cm.get_cmap('rainbow', 10), add_colorbar=False)
ax.set_title('')
ax.set_xlabel('$n_{'+species+'}$ ($cm^{-3}$)')
ax.set_ylabel('$T_{'+species+'}$ (eV)')
ax.set_position((left+row1_width+wspace, bottom, row1_width, height))
util.format_axes(ax, time=False)
# Create a colorbar that is aware of the image's new position
divider = make_axes_locatable(ax)
cax = divider.new_horizontal(size="5%", pad=0.1)
fig.add_axes(cax)
cb = fig.colorbar(img, cax=cax, orientation="vertical")
cb.set_label('$\Delta T_{'+species+'}/T_{'+species+'}$ (%)')
cb.ax.minorticks_on()
'''
# Error in the adjusted look-up table
dN_max = (N - N_max) / N * 100.0
dN_adj = (N - N_lut) / N * 100.0
ax = fig.add_subplot(512)
l1 = dN_max.plot(ax=ax,
label='$\Delta n_{' + species + ',Max}/n_{' + species +
',Max}$')
l2 = dN_adj.plot(ax=ax,
label='$\Delta n_{' + species + ',adj}/n_{' + species +
',adj}$')
ax.set_title('')
ax.set_xticklabels([])
ax.set_xlabel('')
ax.set_ylabel('$\Delta n_{' + species + '}/n_{' + species + '}$ (%)')
ax.set_position((left, bottom - gap - height, width, height))
util.format_axes(ax, xaxis='off')
util.add_legend(ax, [l1[0], l2[0]], corner='SE', horizontal=True)
# Deviation in temperature
dt_max = (t - t_max) / t * 100.0
dt_adj = (t - t_lut) / t * 100.0
ax = fig.add_subplot(513)
l1 = dt_max.plot(ax=ax,
label='$\Delta T_{' + species + ',Max}/T_{' + species +
',Max}$')
l2 = dt_adj.plot(ax=ax,
label='$\Delta T_{' + species + ',adj}/T_{' + species +
',adj}$')
ax.set_title('')
ax.set_xticklabels([])
ax.set_xlabel('')
ax.set_ylabel('$\Delta T_{' + species + '}/T_{' + species + '}$ (%)')
ax.set_ylim(-1, 2.5)
ax.set_position((left, bottom - gap - 2 * height - hspace, width, height))
util.format_axes(ax, xaxis='off')
util.add_legend(ax, [l1[0], l2[0]], corner='NE', horizontal=True)
# Deviation in entropy
ds_moms = (s - s_max_moms) / s * 100.0
ds_m = (s - s_max) / s * 100.0
ds_adj = (s - s_lut) / s * 100.0
ax = fig.add_subplot(514)
l1 = ds_m.plot(ax=ax,
label='$\Delta s_{' + species + ',Max}/s_{' + species +
',Max}$')
l2 = ds_adj.plot(ax=ax,
label='$\Delta s_{' + species + ',adj}/s_{' + species +
',adj}$')
l3 = ds_moms.plot(ax=ax,
label='$\Delta s_{' + species + ',moms}/s_{' + species +
',moms}$')
ax.set_title('')
ax.set_xticklabels([])
ax.set_xlabel('')
ax.set_ylabel('$\Delta s_{' + species + '}/s_{' + species + '}$ (%)')
ax.set_ylim(-9, 2.5)
ax.set_position(
(left, bottom - gap - 3 * height - 2 * hspace, width, height))
util.format_axes(ax, xaxis='off')
util.add_legend(ax, [l1[0], l2[0], l3[0]], corner='SE', horizontal=True)
# Deviation in velocity-space entropy
dsv_max = (sv - sv_max) / sv * 100.0
dsv_adj = (sv - sv_lut) / sv * 100.0
ax = fig.add_subplot(515)
l1 = dsv_max.plot(ax=ax,
label='$\Delta s_{V,' + species + ',Max}/s_{V,' +
species + ',Max}$')
l2 = dsv_adj.plot(ax=ax,
label='$\Delta s_{V,' + species + ',adj}/s_{V,' +
species + ',adj}$')
ax.set_title('')
ax.set_xlabel('')
ax.set_ylabel('$\Delta s_{V,' + species + '}/s_{V,' + species + '}$ (%)')
ax.set_position(
(left, bottom - gap - 4 * height - 3 * hspace, width, height))
util.format_axes(ax)
util.add_legend(ax, [l1[0], l2[0]], corner='SE', horizontal=True)
fig.suptitle('Maxwellian Look-up Table')
plt.setp(fig.axes[2:], xlim=mdates.date2num([start_date, end_date]))
return fig, 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