def thermal_pressure_profiler(hf, step=-1):
"""In Pa"""
try:
hybrid_n = hr(hf, 'np_tot')
except NoSuchVariable:
hybrid_n = hr(hf, 'np')
grid_points = hybrid_n.para['grid_points']
n = hybrid_n.get_timestep(step)[-1]
ndata = n_km2cm(n)
try:
hybrid_temp = hr(hf, 'temp_tot')
except NoSuchVariable:
hybrid_temp = hr(hf, 'temp_p')
t = hybrid_temp.get_timestep(step)[-1]
tdata = t_K(t)
# in Pa not pPa
thermal_p_data = pressure_pPa(ndata, tdata) * 1e-12
p_therm = profile(points, grid_points, thermal_p_data)
return p_therm
def insert_beta_plot(fig, ax, prefix, cax):
hn = hr(prefix, 'np')
para = hn.para
n = hn.get_timestep(-1)[-1]
try:
T = hr(prefix, 'temp_tot').get_timestep(-1)[-1]
except NoSuchVariable:
T = hr(prefix, 'temp_p').get_timestep(-1)[-1]
B = hr(prefix, 'bt').get_timestep(-1)[-1]
# Convert units
n = n / (1000.0**3) # 1/km^3 -> 1/m^3
T = 1.60218e-19 * T # eV -> J
B = 1.6726219e-27 / 1.60217662e-19 * B # proton gyrofrequency -> T
# Compute B \cdot B
B2 = np.sum(B**2, axis=-1)
# Compute plasma beta
mu_0 = value('mag. constant')
assert unit('mag. constant') == 'N A^-2'
data = n * T / (B2 / (2 * mu_0))
m, x, y, s = beta_plot2(ax, data, para, 'xy', mccomas=True)
if cax != 'None':
fig.colorbar(m, cax=cax, orientation='horizontal')
def pressure_profiler(hf, step=-1):
try:
hybrid_n = hr(hf, 'np_tot')
except NoSuchVariable:
hybrid_n = hr(hf, 'np')
grid_points = hybrid_n.para['grid_points']
n = hybrid_n.get_timestep(step)[-1]
ndata = n_km2cm(n)
try:
hybrid_temp = hr(hf, 'temp_tot')
except NoSuchVariable:
hybrid_temp = hr(hf, 'temp_p')
t = hybrid_temp.get_timestep(step)[-1]
tdata = t_K(t)
thermal_p_data = pressure_pPa(ndata, tdata)
hybrid_b = hr(hf, 'bt')
b = hybrid_b.get_timestep(step)[-1]
bdata = b_pressure(b)
p_therm = profile(points, grid_points, thermal_p_data)
p_b = profile(points, grid_points, bdata)
return zip(p_therm, p_b)
def plot_traj_context(fig, ax_xy, ax_xz, prefix, traj, size, mccomas=False):
hybrid_np_CH4 = hr(prefix, 'np_CH4')
np_3d = hybrid_np_CH4.get_timestep(-1)[-1]
hh.direct_plot(fig,
ax_xy,
np_3d,
hybrid_np_CH4.para,
'xy',
norm=LogNorm(),
fontsize=size,
mccomas=mccomas)
hh.direct_plot(fig,
ax_xz,
np_3d,
hybrid_np_CH4.para,
'xz',
norm=LogNorm(),
fontsize=size,
mccomas=mccomas)
traj = traj / 1187.
if mccomas:
traj[:, 0] = -traj[:, 0]
traj[:, 1] = -traj[:, 1]
x = traj[:, 0]
y = traj[:, 1]
z = traj[:, 2]
ax_xy.plot(x, y, color='black', linewidth=2, scalex=False, scaley=False)
ax_xz.plot(x, z, color='black', linewidth=2, scalex=False, scaley=False)
def beta_data(prefix):
hn = hr(prefix, 'np')
para = hn.para
n = hn.get_timestep(-1)[-1]
T = hr(prefix, 'temp_p').get_timestep(-1)[-1]
B = hr(prefix, 'bt').get_timestep(-1)[-1]
# Convert units
n = n / (1000.0**3) # 1/km^3 -> 1/m^3
T = 1.60218e-19 * T # eV -> J
B = 1.6726219e-27 / 1.60217662e-19 * B # proton gyrofrequency -> T
# Compute B \cdot B
B2 = np.sum(B**2, axis=-1)
# Compute plasma beta
data = n * T / (B2 / (2 * 1.257e-6))
return para, data
def profiler(hf, step=-1):
hybrid = hr(hf, var)
grid_points = hybrid.para['grid_points']
v = hybrid.get_timestep(step)[-1]
if modifier is not None:
data = modifier(v)
else:
data = v
return profile(points, grid_points, data)
def magnetic_pressure_profiler(hf, step=-1):
hybrid_b = hr(hf, 'bt')
b = hybrid_b.get_timestep(step)[-1]
# in Pa not pPa
bdata = b_pressure(b) * 1e-12
grid_points = hybrid_b.para['grid_points']
p_b = profile(points, grid_points, bdata)
return p_b
def plot_1d_variable(fig, ax, args):
# Get upstream values
para = hp(args.prefix, force_version=args.force_version).para
B0 = para['b0_init'] # T (yes, b0_init is already in Tesla. No need to convert.)
n0 = para['nf_init']/1000**3 # m^-3
## Handy parameters common to most plots
c = 3e8 # speed of light in m/s
q = 1.602e-19 # C
m = para['ion_amu']*1.6726e-27 # kg
q_over_m = q/m # C/kg
e0 = 8.854e-12 # F/m
mu_0 = 1.257e-6
# Some upstream parameters
omega_pi = np.sqrt(n0*q*q_over_m/e0) # rad/s
lambda_i = c/omega_pi # m
omega_ci = q_over_m*B0 # rad/s
## Special cases first, the general case is the else block at the bottom
if args.variable.name == 'pressure':
steps, times, n = get_1d_scalar(hr(args.prefix, 'np_tot', force_version=args.force_version))
_, _, T = get_1d_scalar(hr(args.prefix, 'temp_tot', force_version=args.force_version))
# Convert units
n = n/(1000.0**3) # 1/km^3 -> 1/m^3
T = q * T # eV -> J
x = 1000*para['qx']/lambda_i # position in units of lambda_i
t = times*omega_ci # time in units of omega_ci^-1
pressure = n*T
mesh = ax.pcolormesh(t, x, pressure.T,
cmap=args.colormap,
norm=args.norm,
vmin=args.vmin,
vmax=args.vmax)
ax.set_xlim(args.xlim)
ax.set_ylim(args.ylim)
elif args.variable.name == 'beta':
steps, times, n = get_1d_scalar(hr(args.prefix, 'np_tot', force_version=args.force_version))
_, _, T = get_1d_scalar(hr(args.prefix, 'temp_tot', force_version=args.force_version))
_, _, B = get_1d_vector(hr(args.prefix, 'bt', force_version=args.force_version))
# Convert units
n = n/(1000.0**3) # 1/km^3 -> 1/m^3
T = q * T # eV -> J
B = B/q_over_m # ion gyrofrequency -> T
# Compute B \cdot B
B2 = np.sum(B**2, axis=-1)
# Compute plasma beta
beta = n*T/(B2/(2*mu_0))
x = 1000*para['qx']/lambda_i # position in units of lambda_i
t = times*omega_ci # time in units of omega_ci^-1
mesh = ax.pcolormesh(t, x, beta.T,
cmap=args.colormap,
norm=args.norm,
vmin=args.vmin,
vmax=args.vmax)
ax.set_xlim(args.xlim)
ax.set_ylim(args.ylim)
elif args.variable.name == 'bmag':
steps, times, B = get_1d_magnetude(hr(args.prefix, 'bt', force_version=args.force_version))
B = B/q_over_m # ion gyrofrequency -> T
x = 1000*para['qx']/lambda_i # position in units of lambda_i
t = times*omega_ci # time in units of omega_ci^-1
mesh = ax.pcolormesh(t, x, B.T,
cmap=args.colormap,
norm=args.norm,
vmin=args.vmin,
vmax=args.vmax)
ax.set_xlim(args.xlim)
ax.set_ylim(args.ylim)
elif args.variable.name == 'brat':
steps, times, B = get_1d_magnetude(hr(args.prefix, 'bt', force_version=args.force_version))
B = B/q_over_m # ion gyrofrequency -> T
B = B/para['b0_init'] # Normalize
x = 1000*para['qx']/lambda_i # position in units of lambda_i
t = times*omega_ci # time in units of omega_ci^-1
mesh = ax.pcolormesh(t, x, B.T,
cmap=args.colormap,
norm=args.norm,
vmin=args.vmin,
vmax=args.vmax)
ax.set_xlim(args.xlim)
ax.set_ylim(args.ylim)
elif args.variable.name == 'upmag':
steps, times, up = get_1d_magnetiude(hr(args.prefix, 'up', force_version=args.force_version))
x = 1000*para['qx']/lambda_i # position in units of lambda_i
t = times*omega_ci # time in units of omega_ci^-1
mesh = ax.pcolormesh(t, x, up.T,
cmap=args.colormap,
norm=args.norm,
vmin=args.vmin,
vmax=args.vmax)
ax.set_xlim(args.xlim)
ax.set_ylim(args.ylim)
else:
h = hr(args.prefix,args.variable.name, force_version=args.force_version)
var_sanity_check(h.isScalar, args.variable.coordinate)
if h.isScalar:
steps, times, data = get_1d_scalar(h)
else:
steps, times, data = get_1d_vector(h)
data = data[:,:,args.variable.coordinate]
x = 1000*para['qx']/lambda_i # position in units of lambda_i
t = times*omega_ci # time in units of omega_ci^-1
mesh = ax.pcolormesh(t, x, data.T,
cmap=args.colormap,
norm=args.norm,
vmin=args.vmin,
vmax=args.vmax)
ax.set_xlim(args.xlim)
ax.set_ylim(args.ylim)
ax.format_coord = build_pcolormesh_format_coord(mesh)
return mesh
def plot_variable(fig1,fig2, ax1,ax2, args):
## Special cases first, the general case is direct_plot() at the bottom
if args.variable.name == 'bs':
hup = hr(args.prefix,'up')
hn_tot = hr(args.prefix,'np')
hn_h = hr(args.prefix,'np_He')
hn_ch4 = hr(args.prefix,'np_CH4')
ux = hup.get_timestep(args.stepnum)[-1]
n_tot = hn_tot.get_timestep(args.stepnum)[-1]
n_h = hn_h.get_timestep(args.stepnum)[-1]
n_ch4 = hn_ch4.get_timestep(args.stepnum)[-1]
ux = ux[:,:,:,0]
para = hup.para
bs_hi_plot(fig1, ax1, n_tot, n_h, n_ch4,ux, 401, 2.7e12, para, 'xy',
mccomas=args.mccomas,
titlesize=args.titlesize,
labelsize=args.labelsize,
ticklabelsize=args.ticklabelsize)
bs_hi_plot(fig2, ax2, n_tot, n_h, n_ch4,ux, 401, 2.7e12, para, 'xz',
mccomas=args.mccomas,
titlesize=args.titlesize,
labelsize=args.labelsize,
ticklabelsize=args.ticklabelsize)
elif args.variable.name == 'pressure':
try:
hn = hr(args.prefix, 'np_tot')
except NoSuchVariable:
hn = hr(args.prefix, 'np')
para = hn.para
n = hn.get_timestep(args.stepnum)[-1]
try:
T = hr(args.prefix, 'temp_tot').get_timestep(args.stepnum)[-1]
except NoSuchVariable:
T = hr(args.prefix, 'temp_p').get_timestep(args.stepnum)[-1]
# Convert units
n = n/(1000.0**3) # 1/km^3 -> 1/m^3
T = 1.60218e-19 * T # eV -> J
data = n*T
m1, X1, Y1, C1 = direct_plot(fig1, ax1, data, para, 'xy',
cmap=args.colormap,
norm=args.norm,
vmin=args.vmin,
vmax=args.vmax,
mccomas=args.mccomas,
titlesize=args.titlesize,
labelsize=args.labelsize,
ticklabelsize=args.ticklabelsize,
cbtitle=args.units)
m2, X2, Y2, C2 = direct_plot(fig2, ax2, data, para, 'xz',
cmap=args.colormap,
norm=args.norm,
vmin=args.vmin,
vmax=args.vmax,
mccomas=args.mccomas,
titlesize=args.titlesize,
labelsize=args.labelsize,
ticklabelsize=args.ticklabelsize,
cbtitle=args.units)
elif args.variable.name == 'beta':
hn = hr(args.prefix, 'np')
para = hn.para
n = hn.get_timestep(args.stepnum)[-1]
try:
T = hr(args.prefix, 'temp_tot').get_timestep(args.stepnum)[-1]
except NoSuchVariable:
T = hr(args.prefix, 'temp_p').get_timestep(args.stepnum)[-1]
B = hr(args.prefix, 'bt').get_timestep(args.stepnum)[-1]
# Convert units
n = n/(1000.0**3) # 1/km^3 -> 1/m^3
T = 1.60218e-19 * T # eV -> J
B = para['ion_amu']*1.6726219e-27/1.60217662e-19 * B # ion gyrofrequency -> T
# Compute B \cdot B
B2 = np.sum(B**2, axis=-1)
# Compute plasma beta
data = n*T/(B2/(2*1.257e-6))
m1, X1, Y1, C1 = beta_plot2(ax1, data, para, 'xy', mccomas=args.mccomas)
m2, X2, Y2, C2 = beta_plot2(ax2, data, para, 'xz', mccomas=args.mccomas)
fig1.colorbar(m1, ax=ax1)
fig2.colorbar(m2, ax=ax2)
args.variable = 'Plasma Beta'
elif args.variable.name == 'bmag':
hb = hr(args.prefix, 'bt')
para = hb.para
B = hb.get_timestep(args.stepnum)[-1]
B = para['ion_amu']*1.6726219e-27/1.60217662e-19 * B # ion gyrofrequency -> T
Bmag = np.sqrt(np.sum(B**2, axis=-1))
data = Bmag
m1, X1, Y1, C1 = direct_plot(fig1, ax1, data, para, 'xy', cmap=args.colormap,
norm=args.norm,
vmin=args.vmin,
vmax=args.vmax,
mccomas=args.mccomas,
titlesize=args.titlesize,
labelsize=args.labelsize,
ticklabelsize=args.ticklabelsize)
m2, X2, Y2, C2 = direct_plot(fig2, ax2, data, para, 'xz',
cmap=args.colormap,
norm=args.norm,
vmin=args.vmin,
vmax=args.vmax,
mccomas=args.mccomas,
titlesize=args.titlesize,
labelsize=args.labelsize,
ticklabelsize=args.ticklabelsize)
elif args.variable.name == 'fmach':
hn = hr(args.prefix, 'np')
para = hn.para
n = hn.get_timestep(args.stepnum)[-1]
T = hr(args.prefix, 'temp_tot').get_timestep(args.stepnum)[-1]
B = hr(args.prefix, 'bt').get_timestep(args.stepnum)[-1]
u = hr(args.prefix, 'up').get_timestep(args.stepnum)[-1]
ux = -u[:,:,:,0]
n = n/(1000.0**3) # 1/km^3 -> 1/m^3
T = 1.60218e-19 * T # eV -> J
B = para['ion_amu']*1.6726219e-27/1.60217662e-19 * B # ion gyrofrequency -> T
B2 = np.sum(B**2, axis=-1)
c = 3e8 # m/s
mu0 = 1.257e-6 # H/m
mp = 1.602e-19 # kg
gamma = 3
# Upstream alfven velocity
us_va = np.sqrt(B2[-1,0,0]/(mu0*mp*n[-1,0,0]))
# Upstream ion acousitic speed
us_vs = np.sqrt(gamma*T[-1,0,0]/mp)
# Upstream fastmode velocity
us_vf = c*np.sqrt((us_vs**2 + us_va**2)/(c**2 + us_va**2))
data = ux/us_vf
m1, X1, Y1, C1 = direct_plot(fig1, ax1, data, para, 'xy',
cmap=args.colormap,
norm=args.norm,
vmin=args.vmin,
vmax=args.vmax,
mccomas=args.mccomas,
titlesize=args.titlesize,
labelsize=args.labelsize,
ticklabelsize=args.ticklabelsize)
m2, X2, Y2, C2 = direct_plot(fig2, ax2, data, para, 'xz',
cmap=args.colormap,
norm=args.norm,
vmin=args.vmin,
vmax=args.vmax,
mccomas=args.mccomas,
titlesize=args.titlesize,
labelsize=args.labelsize,
ticklabelsize=args.ticklabelsize)
elif args.variable.name == 'ratio':
h = hr(args.prefix, 'np')
ch4 = hr(args.prefix, 'np_CH4')
h_data = h.get_timestep(args.stepnum)[-1]
ch4_data = ch4.get_timestep(args.stepnum)[-1]
para = h.para
ratio_plot(fig1, ax1, h_data, ch4_data, para, 'xy',
norm=args.norm,
mccomas=args.mccomas,
titlesize=args.titlesize,
labelsize=args.labelsize,
ticklabelsize=args.ticklabelsize)
ratio_plot(fig2, ax2, h_data, ch4_data, para, 'xz',
norm=args.norm,
mccomas=args.mccomas,
titlesize=args.titlesize,
labelsize=args.labelsize,
ticklabelsize=args.ticklabelsize)
elif args.variable.name == 'upmag':
h = hr(args.prefix,'up')
data = h.get_timestep(args.stepnum)[-1]
data = np.linalg.norm(data, axis=-1)
para = h.para
m1, X1, Y1, C1 = direct_plot(fig1, ax1, data, para, 'xy',
cmap=args.colormap,
norm=args.norm,
vmin=args.vmin,
vmax=args.vmax,
mccomas=args.mccomas,
titlesize=args.titlesize,
labelsize=args.labelsize,
ticklabelsize=args.ticklabelsize,
cbtitle=args.units)
m2, X2, Y2, C2 = direct_plot(fig2, ax2, data, para, 'xz',
cmap=args.colormap,
norm=args.norm,
vmin=args.vmin,
vmax=args.vmax,
mccomas=args.mccomas,
titlesize=args.titlesize,
labelsize=args.labelsize,
ticklabelsize=args.ticklabelsize,
cbtitle=args.units)
else:
h = hr(args.prefix,args.variable.name)
var_sanity_check(h.isScalar, args.variable.coordinate)
data = h.get_timestep(args.stepnum)[-1]
if not h.isScalar:
data = data[:,:,:,args.variable.coordinate]
if str(args.variable).startswith('bt'):
data = h.para['ion_amu']*1.6726219e-27/1.60217662e-19 * data # ion gyrofrequency -> T
para = h.para
m1, X1, Y1, C1 = direct_plot(fig1, ax1, data, para, 'xy',
cmap=args.colormap,
norm=args.norm,
vmin=args.vmin,
vmax=args.vmax,
mccomas=args.mccomas,
titlesize=args.titlesize,
labelsize=args.labelsize,
ticklabelsize=args.ticklabelsize,
cbtitle=args.units)
m2, X2, Y2, C2 = direct_plot(fig2, ax2, data, para, 'xz',
cmap=args.colormap,
norm=args.norm,
vmin=args.vmin,
vmax=args.vmax,
mccomas=args.mccomas,
titlesize=args.titlesize,
labelsize=args.labelsize,
ticklabelsize=args.ticklabelsize,
cbtitle=args.units)
for p in paras:
grids.append(p['grid_points'])
def profile(interp_points, grid_points, grid_data):
rgi = RegularGridInterpolator(points=grid_points,
values=grid_data,
bounds_error=False)
return rgi(interp_points)
points, o = NH_tools.trajectory(NH_tools.close_start, NH_tools.close_end, 60.)
profiles = []
for pre, grid in zip(prefixes, grids):
v = hr(pre, 'up').get_last_timestep()[-1]
vdata = np.linalg.norm(v, axis=-1)
profiles.append(profile(points, grid, vdata))
plt.style.use('pluto-paper')
fig, vax = plt.subplots(figsize=figaspect(0.6))
if args.which == 'shell':
vax.set_title(
"Simulation Flyby Velocity Profiles\nWith SW shell distribution")
elif args.which == 'no-shell':
vax.set_title(
"Simulation Flyby Velocity Profiles\nWithout SW shell distribution")
else:
vax.set_title("Simulation Flyby Velocity Profiles")
vax.set_xlabel('X ($R_p$)')
#!/usr/bin/python
import argparse
import HybridHelper as hh
from HybridReader2 import HybridReader2 as hr
import FortranFile as ff
import matplotlib.pyplot as plt
import numpy
hh.parser.add_argument('filenum',
type=int,
help='Which time_stepping_error file to open')
args = hh.parser.parse_args()
params = hr(args.prefix, args.variable).para
# open desired FortranFile
f = ff.FortranFile('./' + args.prefix + '/c.time_stepping_error_' +
str(args.filenum) + '.dat')
def read_entry(f):
entry = {}
entry['step'] = f.readInts()
assert len(entry['step']) == 1
entry['step'] = entry['step'][0]
entry['rank'] = f.readInts()
assert len(entry['rank']) == 1
entry['rank'] = int(entry['rank'][0])
entry['indicies'] = f.readInts(
) - 1 # subtract 1 to convert from 1-based indexing to 0-based indexing
entry['coords'] = f.readReals()
def _espec_v4(data, options=None):
if options is None:
options= {'colorbars':['Blues','Greens','Reds']}
# Load data from IDL
spectrograms_by_species = data['spectrograms_by_species']
t = data['times']
x = data['positions'][0,:]
y = data['positions'][1,:]
z = data['positions'][2,:]
o = data['orientations']
ebins = data['ebins']
# Separate parts
H = spectrograms_by_species[:,:,0]
He = spectrograms_by_species[:,:,1]
CH4 = spectrograms_by_species[:,:,2]
cnt_arr = H+He+CH4
# Build masked arrays for plotting the dominant species
mH = masked_array(cnt_arr, mask=(~logical_and(H>He,H>CH4)))
mHe = masked_array(cnt_arr, mask=(~logical_and(He>H,He>CH4)))
mCH4 = masked_array(cnt_arr, mask=~logical_and(CH4>H,CH4>He))
# Setup subplot and colorbar axes
# fig = plt.figure(figsize = (rcParams['figure.figsize'][0], 2*rcParams['figure.figsize'][1]))
# gs = gridspec.GridSpec(8,1)
# ax_spec = plt.subplot(gs[0,0])
# ax_theta = plt.subplot(gs[1,0], sharex=ax_spec)
# ax_phi = plt.subplot(gs[2,0], sharex=ax_spec)
# ax_spin = plt.subplot(gs[3,0], sharex=ax_spec)
#
# ax_xy = plt.subplot(gs[4:6,0])
# ax_xz = plt.subplot(gs[6:8,0], sharex=ax_xy)
fig, (ax_spec, ax_theta, ax_phi, ax_spin, ax_xy, ax_xz) = plt.subplots(nrows=6, sharex=True,
gridspec_kw={'height_ratios':[1,1,1,1,2,3]},
figsize=(rcParams['figure.figsize'][0], 2.25*rcParams['figure.figsize'][1]))
fig.subplots_adjust(right=0.8, hspace=0.05)
spec_pos = ax_spec.get_position()
cbar_CH4 = fig.add_axes([spec_pos.x1+.01, spec_pos.y0+.01, 0.1/3, spec_pos.height-.02])
cbar_CH4_pos = cbar_CH4.get_position()
cbar_He = fig.add_axes([cbar_CH4_pos.x1, spec_pos.y0+.01, 0.1/3, spec_pos.height-.02])
cbar_He_pos = cbar_He.get_position()
cbar_H = fig.add_axes([cbar_He_pos.x1, spec_pos.y0+.01, 0.1/3,spec_pos.height-.02])
# Plot spectrograms and make colorbars
Hhist = ax_spec.pcolormesh(t, ebins, mH, norm=LogNorm(), cmap=options['colorbars'][0],
vmin=1e-3, vmax=1e4)
Hcb = fig.colorbar(Hhist, cax=cbar_H)
Hcb.ax.minorticks_on()
Hehist = ax_spec.pcolormesh(t, ebins, mHe, norm=LogNorm(), cmap=options['colorbars'][1],
vmin=1e-3, vmax=1e4)
Hecb = fig.colorbar(Hehist, cax=cbar_He, format="")
CH4hist = ax_spec.pcolormesh(t, ebins, mCH4, norm=LogNorm(), cmap=options['colorbars'][2],
vmin=1e-3, vmax=1e4)
CH4cb = fig.colorbar(CH4hist, cax=cbar_CH4, format="")
# Plot orientations
ax_theta.plot(t, o[0], marker='o', markersize=1.3, linestyle='None', color='black')
ax_phi.plot(t, o[1], marker='o', markersize=1.3, linestyle='None', color='black')
ax_spin.plot(t, o[2], marker='o', markersize=1.3, linestyle='None', color='black')
ax_theta.set_ylim(-180,180)
ax_phi.set_ylim(-180,180)
ax_spin.set_ylim(-180,180)
# Plot positions
h = hr('/home/nathan/data/2017-Wed-Aug-30/pluto-1/databig','np')
n = h.get_last_timestep()[-1]
# Get grid spacing
qx = h.para['qx']
qy = h.para['qy']
qzrange = h.para['qzrange']
# Find the center index of the grid
cx = h.para['nx']/2
cy = h.para['ny']/2
cz = h.para['zrange']/2
# the offset of pluto from the center isn't always availible
try:
po = h.para['pluto_offset']
except KeyError:
print("Couldn't get pluto_offset. It has been assumed to be 30, but it probably isn't.")
po = 30
# Set constatnt for Pluto radius
Rp = 1187. # km
# Shift grid so that Pluto lies at (0,0,0) and convert from km to Rp
qx = (qx - qx[len(qx)/2 + po])/Rp
qy = (qy - qy[len(qy)/2])/Rp
qzrange = (qzrange - qzrange[len(qzrange)/2])/Rp
qt = [NH_tools.time_at_pos(x*Rp) for x in qx]
T,Y = np.meshgrid(qt,qy)
ax_xy.pcolormesh(T,Y,n[:,:,cz].transpose(), cmap='viridis', norm=LogNorm())
T,Z = np.meshgrid(qt,qzrange)
ax_xz.pcolormesh(T,Z,n[:,cy,:].transpose(), cmap='viridis', norm=LogNorm())
ax_xy.plot(t, y/Rp-np.max(qy), color='black', linewidth=2)
ax_xz.plot(t, np.zeros(z.shape), color='black', linewidth=2)
# Set title, labels and adjust figure
ax_spec.set_xlim(np.min(t), np.max(t))
ax_spec.set_ylim(5e1, 1e4)
ax_spec.set_title("Synthetic SWAP Energy Spectrogram", y=1.4)
Hecb.ax.set_title('COIN (Hz)', fontdict={'fontsize':'small'})
ax_spec.set_yscale('log')
ax_spec.set_ylabel('Energy/Q ($eV/q$)')
ax_theta.set_ylabel('Sun Theta\nAngle (deg)')
ax_phi.set_ylabel('Sun Phi\nAngle (deg)')
ax_spin.set_ylabel('Spin\nAngle (deg)')
ax_xz.set_xlabel('Time (UTC)')
ax_xy.set_ylabel('Y ($R_p$)')
ax_xz.set_ylabel('Z ($R_p$)')
ax_xy.set_ylim(np.min(qy), np.max(qy))
ax_xz.set_ylim(1.5*np.min(qy), 1.5*np.max(qy))
ax_xz.set_xticklabels([sp.timout(et, 'HR:MN:SC') for et in ax_spec.get_xticks()], rotation=-20, horizontalalignment='left')
ax_twin = ax_spec.twiny()
ax_twin.set_xlabel('X ($R_p$)')
ax_twin.set_xlim(*ax_spec.get_xlim())
ax_twin.set_xticks(ax_spec.get_xticks())
ax_twin.set_xticklabels(['{0:.2f}'.format(NH_tools.pos_at_time(et)/1187) for et in ax_spec.get_xticks()], y=1.05)
# This breaks the convention of previous versions
return fig, None
