def energy_spectrum_multi(bg, spect_info, species='e'):
"""Plot energy spectra for runs with different guide field
"""
if species == 'h':
species = 'H'
bg_str = str(int(bg * 10)).zfill(2)
mimes = np.asarray([25, 100, 400])
tmaxs = np.asarray([114, 114, 102])
fig = plt.figure(figsize=[7, 5])
ax = fig.add_axes([0.15, 0.15, 0.8, 0.8])
COLORS = palettable.tableau.Tableau_10.mpl_colors
ax.set_prop_cycle('color', COLORS)
for mime, tmax in zip(mimes, tmaxs):
run_name = "mime" + str(mime) + "_beta002_bg" + bg_str
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
if species == 'e':
vth = pic_info.vthe
else:
vth = pic_info.vthi
gama = 1.0 / math.sqrt(1.0 - 3 * vth**2)
eth = gama - 1.0
fdir = '../data/spectra/' + run_name + '/'
fname = fdir + 'spectrum-' + species.lower() + '.' + str(tmax)
flog = np.fromfile(fname)
emin_log = math.log10(spect_info["emin"])
emax_log = math.log10(spect_info["emax"])
elog = 10**(np.linspace(emin_log, emax_log, spect_info["nbins"]))
flog *= np.gradient(elog)
elog /= eth
flog /= np.gradient(elog)
nptot = pic_info.nx * pic_info.ny * pic_info.nz * pic_info.nppc
flog /= nptot
ltext = r"$m_i/m_e=" + str(mime) + "$"
ax.loglog(elog, flog, linewidth=3, label=ltext)
ax.legend(loc=3, prop={'size': 16}, ncol=1,
shadow=False, fancybox=False, frameon=False)
if species == 'e':
ax.set_xlim([3E-1, 5E2])
else:
ax.set_xlim([3E-1, 1E3])
ax.set_ylim([1E-11, 1E0])
ax.set_yticks(np.logspace(-10, 0, num=6))
ax.set_xlabel(r'$\varepsilon/\varepsilon_\text{th}$',
fontdict=FONT, fontsize=20)
ax.set_ylabel(r'$f(\varepsilon)$', fontdict=FONT, fontsize=20)
ax.tick_params(labelsize=16)
fpath = "../img/img_high_mime/spectra/"
mkdir_p(fpath)
fname = fpath + "spect_bg" + bg_str + "_" + species + ".pdf"
fig.savefig(fname)
plt.show()
def plot_beta_dist(plot_config, show_plot=True):
"""
Plot plasma beta distribution
"""
pic_run = plot_config["pic_run"]
pic_run_dir = plot_config["pic_run_dir"]
species = plot_config["species"]
tframe = plot_config["tframe"]
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
tinit = pic_info.vthe**2
beta_init = tinit * 2 / pic_info.b0**2
fdir = '../data/temperature_anisotropy/' + pic_run + '/'
fname = fdir + 'fbpara_' + species + '_' + str(tframe) + '.gda'
fdata = np.fromfile(fname, dtype=np.float32)
nbins_beta = int(fdata[0])
beta_bins_edge = fdata[1:nbins_beta+2]
beta_bins_mid = 0.5 * (beta_bins_edge[1:] + beta_bins_edge[:-1])
beta_bins_mid /= beta_init
dtemp = np.diff(beta_bins_edge)
fbpara = fdata[nbins_beta+2:] / dtemp
fname = fdir + 'fbperp_' + species + '_' + str(tframe) + '.gda'
fdata = np.fromfile(fname, dtype=np.float32)
fbperp = fdata[nbins_beta+2:] / dtemp
fig = plt.figure(figsize=[3.25, 2.5])
rect = [0.16, 0.16, 0.79, 0.8]
ax = fig.add_axes(rect)
ax.set_prop_cycle('color', COLORS)
ax.tick_params(bottom=True, top=True, left=True, right=True)
ax.tick_params(axis='x', which='minor', direction='in', bottom=True, top=True)
ax.tick_params(axis='x', which='major', direction='in', bottom=True, top=True)
ax.tick_params(axis='y', which='minor', direction='in')
ax.tick_params(axis='y', which='major', direction='in')
ax.loglog(beta_bins_mid, fbpara, linewidth=1, label=r'$\beta_\parallel$')
ax.loglog(beta_bins_mid, fbperp, linewidth=1, label=r'$\beta_\perp$')
ax.legend(loc=1, prop={'size': 10}, ncol=1,
shadow=False, fancybox=False, frameon=False)
ax.set_xlim([1E-1, 1E5])
ax.tick_params(labelsize=8)
ax.set_xlabel(r'$\beta/\beta_0$', fontsize=10)
ax.set_ylabel(r'$f(\beta)$', fontsize=10)
fdir = '../img/beta_dist/' + pic_run + '/'
mkdir_p(fdir)
fname = fdir + 'beta_dist_' + species + '_' + str(tframe) + '.pdf'
fig.savefig(fname)
if show_plot:
plt.show()
else:
plt.close()
def plot_tratio_dist(plot_config, show_plot=True):
"""
Plot the distribution of the ratio between Tperp and Tpara
"""
pic_run = plot_config["pic_run"]
pic_run_dir = plot_config["pic_run_dir"]
species = plot_config["species"]
tframe = plot_config["tframe"]
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
fdir = '../data/temperature_anisotropy/' + pic_run + '/'
fname = fdir + 'ftratio_' + species + '_' + str(tframe) + '.gda'
fdata = np.fromfile(fname, dtype=np.float32)
nbins_tratio = int(fdata[0])
tratio_bins_edge = fdata[1:nbins_tratio + 2]
tratio_bins_mid = 0.5 * (tratio_bins_edge[1:] + tratio_bins_edge[:-1])
dtemp = np.diff(tratio_bins_edge)
ftratio = fdata[nbins_tratio + 2:] / dtemp
fig = plt.figure(figsize=[3.25, 2.5])
rect = [0.16, 0.16, 0.79, 0.8]
ax = fig.add_axes(rect)
ax.set_prop_cycle('color', COLORS)
ax.tick_params(bottom=True, top=True, left=True, right=True)
ax.tick_params(axis='x',
which='minor',
direction='in',
bottom=True,
top=True)
ax.tick_params(axis='x',
which='major',
direction='in',
bottom=True,
top=True)
ax.tick_params(axis='y', which='minor', direction='in')
ax.tick_params(axis='y', which='major', direction='in')
ax.loglog(tratio_bins_mid, ftratio, linewidth=1)
ax.set_xlim([5E-2, 2E1])
ax.tick_params(labelsize=8)
ax.set_xlabel(r'$T_\perp/T_\parallel$', fontsize=10)
ax.set_ylabel(r'$f(T_\perp/T_\parallel)$', fontsize=10)
fdir = '../img/tratio_dist/' + pic_run + '/'
mkdir_p(fdir)
fname = fdir + 'tratio_dist_' + species + '_' + str(tframe) + '.pdf'
fig.savefig(fname)
if show_plot:
plt.show()
else:
plt.close()
def main():
"""business logic for when running this module as the primary one!"""
args = get_cmd_args()
run_name = args.run_name
run_dir = args.run_dir
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
if args.multi_frames:
ncores = multiprocessing.cpu_count()
tframes = range(pic_info.ntf)
Parallel(n_jobs=ncores)(delayed(process_input)(run_dir,
run_name,
tframe)
for tframe in tframes)
else:
combine_energy_spectrum(run_dir, run_name,
args.tframe, species=args.species)
def main():
"""business logic for when running this module as the primary one!"""
args = get_cmd_args()
run_name = args.run_name
run_dir = args.run_dir
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
if args.multi_frames:
ncores = multiprocessing.cpu_count()
tframes = range(pic_info.ntf)
Parallel(n_jobs=ncores)(
delayed(process_input)(run_dir, run_name, tframe)
for tframe in tframes)
else:
combine_energy_spectrum(run_dir,
run_name,
args.tframe,
species=args.species)
def plot_tratio_dist(plot_config, show_plot=True):
"""
Plot the distribution of the ratio between Tperp and Tpara
"""
pic_run = plot_config["pic_run"]
pic_run_dir = plot_config["pic_run_dir"]
species = plot_config["species"]
tframe = plot_config["tframe"]
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
fdir = '../data/temperature_anisotropy/' + pic_run + '/'
fname = fdir + 'ftratio_' + species + '_' + str(tframe) + '.gda'
fdata = np.fromfile(fname, dtype=np.float32)
nbins_tratio = int(fdata[0])
tratio_bins_edge = fdata[1:nbins_tratio+2]
tratio_bins_mid = 0.5 * (tratio_bins_edge[1:] + tratio_bins_edge[:-1])
dtemp = np.diff(tratio_bins_edge)
ftratio = fdata[nbins_tratio+2:] / dtemp
fig = plt.figure(figsize=[3.25, 2.5])
rect = [0.16, 0.16, 0.79, 0.8]
ax = fig.add_axes(rect)
ax.set_prop_cycle('color', COLORS)
ax.tick_params(bottom=True, top=True, left=True, right=True)
ax.tick_params(axis='x', which='minor', direction='in', bottom=True, top=True)
ax.tick_params(axis='x', which='major', direction='in', bottom=True, top=True)
ax.tick_params(axis='y', which='minor', direction='in')
ax.tick_params(axis='y', which='major', direction='in')
ax.loglog(tratio_bins_mid, ftratio, linewidth=1)
ax.set_xlim([5E-2, 2E1])
ax.tick_params(labelsize=8)
ax.set_xlabel(r'$T_\perp/T_\parallel$', fontsize=10)
ax.set_ylabel(r'$f(T_\perp/T_\parallel)$', fontsize=10)
fdir = '../img/tratio_dist/' + pic_run + '/'
mkdir_p(fdir)
fname = fdir + 'tratio_dist_' + species + '_' + str(tframe) + '.pdf'
fig.savefig(fname)
if show_plot:
plt.show()
else:
plt.close()
def combine_energy_spectrum(run_dir, run_name, tframe, species='e'):
"""Combine particle energy spectrum from different mpi_rank
Args:
run_dir: PIC simulation directory
run_name: PIC simulation run name
tframe: time frame
species: 'e' for electrons, 'H' for ions
"""
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
interval = pic_info.fields_interval
mpi_size = pic_info.topology_x * pic_info.topology_y * pic_info.topology_z
rank = 0
ndata = (NBINS + 3) if INCLUDE_BFIELDS else NBINS
tindex = tframe * interval
fname_pre = run_dir + 'hydro/T.' + str(tindex)
if species == 'h':
species = 'H'
fname_pre += '/spectrum-' + species + 'hydro.' + str(tindex)
fname = fname_pre + '.' + str(rank)
fdata = np.fromfile(fname, dtype=np.float32)
dsz, = fdata.shape
nzone = dsz // ndata
for rank in range(1, mpi_size):
fname = fname_pre + '.' + str(rank)
fdata += np.fromfile(fname, dtype=np.float32)
print("number of zones: %d" % nzone)
flog_tot = np.zeros(NBINS)
for i in range(nzone):
if INCLUDE_BFIELDS:
flog = fdata[i*ndata+3:(i+1)*ndata]
else:
flog = fdata[i*ndata:(i+1)*ndata]
flog_tot += flog
emin_log = math.log10(EMIN)
emax_log = math.log10(EMAX)
elog = 10**(np.linspace(emin_log, emax_log, NBINS))
delog = np.gradient(elog)
flog_tot /= delog
fdir = '../data/spectra/' + run_name + '/'
mkdir_p(fdir)
fname = fdir + 'spectrum-' + species.lower() + '.' + str(tframe)
flog_tot.tofile(fname)
def combine_energy_spectrum(run_dir, run_name, tframe, species='e'):
"""Combine particle energy spectrum from different mpi_rank
Args:
run_dir: PIC simulation directory
run_name: PIC simulation run name
tframe: time frame
species: 'e' for electrons, 'H' for ions
"""
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
interval = pic_info.fields_interval
mpi_size = pic_info.topology_x * pic_info.topology_y * pic_info.topology_z
rank = 0
ndata = (NBINS + 3) if INCLUDE_BFIELDS else NBINS
tindex = tframe * interval
fname_pre = run_dir + 'hydro/T.' + str(tindex)
if species == 'h':
species = 'H'
fname_pre += '/spectrum-' + species + 'hydro.' + str(tindex)
fname = fname_pre + '.' + str(rank)
fdata = np.fromfile(fname, dtype=np.float32)
dsz, = fdata.shape
nzone = dsz // ndata
for rank in range(1, mpi_size):
fname = fname_pre + '.' + str(rank)
fdata += np.fromfile(fname, dtype=np.float32)
print("number of zones: %d" % nzone)
flog_tot = np.zeros(NBINS)
for i in range(nzone):
if INCLUDE_BFIELDS:
flog = fdata[i * ndata + 3:(i + 1) * ndata]
else:
flog = fdata[i * ndata:(i + 1) * ndata]
flog_tot += flog
emin_log = math.log10(EMIN)
emax_log = math.log10(EMAX)
elog = 10**(np.linspace(emin_log, emax_log, NBINS))
delog = np.gradient(elog)
flog_tot /= delog
fdir = '../data/spectra/' + run_name + '/'
mkdir_p(fdir)
fname = fdir + 'spectrum-' + species.lower() + '.' + str(tframe)
flog_tot.tofile(fname)
def plot_spectrum(plot_config, show_plot=True):
"""Plot power spectrum
Args:
plot_config: plot configuration
"""
pic_run = plot_config["pic_run"]
pic_run_dir = plot_config["pic_run_dir"]
tframe = plot_config["tframe"]
var_name = plot_config["var_name"]
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
tratio = pic_info.particle_interval / pic_info.fields_interval
tindex = tframe * pic_info.fields_interval
fname = pic_run_dir + "vkappa_spectrum/" + var_name + str(tindex) + ".kx"
fx = np.fromfile(fname, dtype=np.float32)
fname = pic_run_dir + "vkappa_spectrum/" + var_name + str(tindex) + ".ky"
fy = np.fromfile(fname, dtype=np.float32)
fname = pic_run_dir + "vkappa_spectrum/" + var_name + str(tindex) + ".kz"
fz = np.fromfile(fname, dtype=np.float32)
fx = fx.reshape((2, -1))
fy = fy.reshape((2, -1))
fz = fz.reshape((2, -1))
fig = plt.figure(figsize=[7, 5])
ax = fig.add_axes([0.17, 0.15, 0.75, 0.8])
ax.loglog(fx[0, :], fx[1, :], linewidth=2, label=r'$x$')
ax.loglog(fy[0, :], fy[1, :], linewidth=2, label=r'$y$')
ax.loglog(fz[0, :], fz[1, :], linewidth=2, label=r'$z$')
ax.legend(loc=1, prop={'size': 16}, ncol=1,
shadow=False, fancybox=False, frameon=False)
ax.tick_params(labelsize=16)
ax.set_xlabel(r'$k_x, k_y, k_z (d_e^{-1})$', fontsize=20)
ax.set_ylabel(r'$f(k_x), f(k_y), f(k_z)$', fontsize=20)
fdir = '../img/power_spectrum_vkappa/' + pic_run + '/'
mkdir_p(fdir)
fname = fdir + var_name + "_" + str(tindex).zfill(5) + ".pdf"
fig.savefig(fname)
if show_plot:
plt.show()
else:
plt.close()
def plot_energy_spectrum(run_name, spect_info, species='e', show_plot=True):
"""Plot particle energy spectrum
Args:
run_name: PIC simulation run name
species: 'e' for electrons, 'H' for ions
spect_info: dictionary for spectra information
"""
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
if species == 'h':
species = 'H'
# ntf = spect_info["tmax"]
ntf = pic_info.ntf
if species == 'e':
vth = pic_info.vthe
else:
vth = pic_info.vthi
gama = 1.0 / math.sqrt(1.0 - 3 * vth**2)
eth = gama - 1.0
emin_log = math.log10(spect_info["emin"])
emax_log = math.log10(spect_info["emax"])
nbins = spect_info["nbins"]
elog = 10**(np.linspace(emin_log, emax_log, nbins))
elog /= eth
nptot = pic_info.nx * pic_info.ny * pic_info.nz * pic_info.nppc
tframes = range(0, ntf - 1)
nframes = len(tframes)
flogs = np.zeros((nframes, nbins))
fig = plt.figure(figsize=[7, 5])
rect = [0.14, 0.16, 0.82, 0.8]
ax = fig.add_axes(rect)
for iframe, tframe in enumerate(tframes):
print("Time frame: %d" % tframe)
fdir = '../data/spectra/' + run_name + '/'
fname = fdir + 'spectrum-' + species.lower() + '.' + str(tframe)
flog = np.fromfile(fname)
flog /= nptot
color = plt.cm.jet(tframe/float(ntf), 1)
flogs[iframe, :] = flog
# ax.loglog(elog, flog, linewidth=2, color=color)
# fmin = np.min(flogs[np.nonzero(flogs)])
fmin = 1E-9
flogs += fmin
fdata = np.diff(np.log10(flogs[:, 400:600]), axis=0).T
ng = 3
kernel = np.ones((ng,ng)) / float(ng*ng)
fdata = signal.convolve2d(fdata, kernel, mode='same')
ax.imshow(fdata, vmin=-1E-1, vmax=1E-1, cmap=plt.cm.seismic,
origin='lower', interpolation='bicubic')
# if species == 'e':
# ax.set_xlim([1E0, 5E2])
# ax.set_ylim([1E-9, 1E2])
# else:
# ax.set_xlim([1E0, 2E3])
# ax.set_ylim([1E-7, 1E4])
ax.tick_params(bottom=True, top=True, left=True, right=False)
ax.tick_params(axis='x', which='minor', direction='in')
ax.tick_params(axis='x', which='major', direction='in')
ax.tick_params(axis='y', which='minor', direction='in')
ax.tick_params(axis='y', which='major', direction='in')
ax.set_xlabel(r'$\varepsilon/\varepsilon_\text{th}$',
fontdict=FONT, fontsize=20)
ax.set_ylabel(r'$f(\varepsilon)$', fontdict=FONT, fontsize=20)
ax.tick_params(labelsize=16)
ename = 'electron' if species == 'e' else 'ion'
fpath = "../img/img_high_mime/spectra/" + ename + "/"
mkdir_p(fpath)
fname = fpath + "spect_time_" + run_name + "_" + species + ".pdf"
fig.savefig(fname)
if show_plot:
plt.show()
else:
plt.close()
def plot_power_spectrum(plot_config, show_plot=True):
"""Plot power spectrum for one variable at one time frame
Args:
plot_config: plot configuration
"""
pic_run = plot_config["pic_run"]
pic_run_dir = plot_config["pic_run_dir"]
tframe = plot_config["tframe"]
var_name = plot_config["var_name"]
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
tindex = tframe * pic_info.fields_interval
fdir = '../data/power_spectrum/' + pic_run + '/power_spectrum_' + var_name + '/'
labels = var_labels()
var_label = labels[var_name]
fname = fdir + var_name + str(tindex) + '.kx'
kx, fkx = read_power_spectrum(fname)
fname = fdir + var_name + str(tindex) + '.ky'
ky, fky = read_power_spectrum(fname)
fname = fdir + var_name + str(tindex) + '.kz'
kz, fkz = read_power_spectrum(fname)
fname = fdir + var_name + str(tindex) + '.para'
kpara, fkpara = read_power_spectrum(fname)
fname = fdir + var_name + str(tindex) + '.perp'
kperp, fkperp = read_power_spectrum(fname)
smime = math.sqrt(pic_info.mime)
dx_de = pic_info.dx_di * smime
dy_de = pic_info.dy_di * smime
dz_de = pic_info.dz_di * smime
k1, k2 = 0.02, 0.5
nkbins = 100
idx = (np.abs(kx - k1)).argmin()
idy = (np.abs(ky - k1)).argmin()
idz = (np.abs(kz - k1)).argmin()
fnorm = max(fkx[idx], fky[idy], fkz[idz]) * 2
kpower = np.logspace(math.log10(k1), math.log10(k2), 100)
fpower1 = kpower**(-5 / 3)
fpower1 *= fnorm / fpower1[0]
fpower2 = kpower**-1.5
fpower2 *= fpower1[0] / fpower2[0]
fpower3 = kpower**-2
fpower3 *= fpower1[0] / fpower3[0]
fig = plt.figure(figsize=[7, 5])
rect = [0.15, 0.16, 0.8, 0.8]
ax = fig.add_axes(rect)
ax.set_prop_cycle('color', COLORS)
ax.loglog(kx, fkx, linewidth=2, label=r'$k_x$')
ax.loglog(ky, fky, linewidth=2, label=r'$k_y$')
ax.loglog(kz, fkz, linewidth=2, label=r'$k_z$')
ax.loglog(kpower,
fpower1,
linewidth=1,
color='k',
linestyle='--',
label=r'$\sim k^{-5/3}$')
ax.loglog(kpower,
fpower2,
linewidth=1,
color='k',
linestyle='-.',
label=r'$\sim k^{-3/2}$')
ax.loglog(kpower,
fpower3,
linewidth=1,
color='k',
linestyle=':',
label=r'$\sim k^{-2}$')
ax.legend(loc=1,
prop={'size': 16},
ncol=1,
shadow=False,
fancybox=False,
frameon=False)
ax.tick_params(bottom=True, top=True, left=True, right=True)
ax.tick_params(axis='x', which='minor', direction='in', top=True)
ax.tick_params(axis='x', which='major', direction='in')
ax.tick_params(axis='y', which='minor', direction='in', left=True)
ax.tick_params(axis='y', which='major', direction='in')
text1 = r'$' + var_label + '$'
ax.text(0.02,
0.05,
text1,
color='k',
fontsize=20,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
ax.set_xlim([1E-2, 1E1])
# ax.set_ylim([1E-7, 2E-1])
ax.set_xlabel(r'$kd_e$', fontsize=20)
ax.set_ylabel(r'$E_{' + var_label + '}(k)$', fontsize=20)
ax.tick_params(labelsize=16)
fdir = '../img/power_spectrum/' + pic_run + '/' + var_name + '/'
mkdir_p(fdir)
fname = fdir + var_name + '_xyz_' + str(tframe) + '.pdf'
fig.savefig(fname)
fig = plt.figure(figsize=[7, 5])
rect = [0.15, 0.16, 0.8, 0.8]
ax = fig.add_axes(rect)
ax.set_prop_cycle('color', COLORS)
ax.loglog(kpara, fkpara, linewidth=2, label=r'$k_\parallel$')
ax.loglog(kperp, fkperp, linewidth=2, label=r'$k_\perp$')
ax.loglog(kpower,
fpower1,
linewidth=1,
color='k',
linestyle='--',
label=r'$\sim k^{-5/3}$')
ax.loglog(kpower,
fpower2,
linewidth=1,
color='k',
linestyle='-.',
label=r'$\sim k^{-3/2}$')
ax.loglog(kpower,
fpower3,
linewidth=1,
color='k',
linestyle=':',
label=r'$\sim k^{-2}$')
ax.legend(loc=1,
prop={'size': 16},
ncol=1,
shadow=False,
fancybox=False,
frameon=False)
ax.tick_params(bottom=True, top=True, left=True, right=True)
ax.tick_params(axis='x', which='minor', direction='in', top=True)
ax.tick_params(axis='x', which='major', direction='in')
ax.tick_params(axis='y', which='minor', direction='in', left=True)
ax.tick_params(axis='y', which='major', direction='in')
text1 = r'$' + var_label + '$'
ax.text(0.02,
0.05,
text1,
color='k',
fontsize=20,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
ax.set_xlim([1E-2, 1E1])
# ax.set_ylim([1E-7, 2E-1])
ax.set_xlabel(r'$kd_e$', fontsize=20)
ax.set_ylabel(r'$E_{' + var_label + '}(k)$', fontsize=20)
ax.tick_params(labelsize=16)
fname = fdir + var_name + '_para_perp_' + str(tframe) + '.pdf'
fig.savefig(fname)
if show_plot:
plt.show()
else:
plt.close('all')
type=int,
help='Time frame for fields')
parser.add_argument('--multi_frames',
action="store_true",
default=False,
help='whether analyzing multiple frames')
return parser.parse_args()
if __name__ == "__main__":
args = get_cmd_args()
run_name = args.run_name
run_dir = args.run_dir
species = args.species
tframe_fields = args.tframe_fields
multi_frames = args.multi_frames
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
tratio = pic_info.particle_interval / pic_info.fields_interval
# plot_ne_jdote(pic_info, run_dir, run_name, 30)
cts = range(pic_info.ntf)
def processInput(job_id):
print job_id
ct = job_id
plot_ne_jdote(pic_info, run_dir, run_name, ct)
# ncores = multiprocessing.cpu_count()
ncores = 8
Parallel(n_jobs=ncores)(delayed(processInput)(ct) for ct in cts)
def rho_bands_2d(plot_config, show_plot=True):
"""Plot densities for energetic particles in the 2D simulation
"""
pic_run = plot_config["pic_run"]
species = plot_config["species"]
tframe = plot_config["tframe"]
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
pic_run_dir = pic_info.run_dir
if species == 'e':
vth = pic_info.vthe
else:
vth = pic_info.vthi
nbins = 1000
ndata = nbins + 3 # including magnetic field
tindex = pic_info.particle_interval * tframe
gama = 1.0 / math.sqrt(1.0 - 3 * vth**2)
eth = gama - 1.0
ebins = np.logspace(-6, 4, nbins)
ebins /= eth
xmin, xmax = 0, pic_info.lx_di
zmin, zmax = -pic_info.lz_di * 0.5, pic_info.lz_di * 0.5
nx, nz = pic_info.nx, pic_info.nz
smime = math.sqrt(pic_info.mime)
dx_de = pic_info.dx_di * smime
dy_de = pic_info.dy_di * smime
dz_de = pic_info.dz_di * smime
kwargs = {
"current_time": tframe,
"xl": 0,
"xr": pic_info.lx_di,
"zb": -pic_info.lz_di,
"zt": pic_info.lz_di
}
# fname = pic_run_dir + "data/Ay.gda"
# x, z, Ay = read_2d_fields(pic_info, fname, **kwargs)
fname = pic_run_dir + "data/bx.gda"
x, z, bx = read_2d_fields(pic_info, fname, **kwargs)
fname = pic_run_dir + "data/by.gda"
x, z, by = read_2d_fields(pic_info, fname, **kwargs)
fname = pic_run_dir + "data/bz.gda"
x, z, bz = read_2d_fields(pic_info, fname, **kwargs)
ib = 1.0 / np.sqrt(bx**2 + by**2 + bz**2)
bx = bx * ib
by = by * ib
bz = bz * ib
kappax = (bx * np.gradient(bx, axis=1) / dx_de +
bz * np.gradient(bx, axis=0) / dz_de)
kappay = (bx * np.gradient(by, axis=1) / dx_de +
bz * np.gradient(by, axis=0) / dz_de)
kappaz = (bx * np.gradient(bz, axis=1) / dx_de +
bz * np.gradient(bz, axis=0) / dz_de)
fname = pic_run_dir + "data/vex.gda"
x, z, vex = read_2d_fields(pic_info, fname, **kwargs)
fname = pic_run_dir + "data/vey.gda"
x, z, vey = read_2d_fields(pic_info, fname, **kwargs)
fname = pic_run_dir + "data/vez.gda"
x, z, vez = read_2d_fields(pic_info, fname, **kwargs)
fname = pic_run_dir + "data/vix.gda"
x, z, vix = read_2d_fields(pic_info, fname, **kwargs)
fname = pic_run_dir + "data/viy.gda"
x, z, viy = read_2d_fields(pic_info, fname, **kwargs)
fname = pic_run_dir + "data/viz.gda"
x, z, viz = read_2d_fields(pic_info, fname, **kwargs)
fname = pic_run_dir + "data/ne.gda"
x, z, ne = read_2d_fields(pic_info, fname, **kwargs)
fname = pic_run_dir + "data/ni.gda"
x, z, ni = read_2d_fields(pic_info, fname, **kwargs)
inrho = 1.0 / (ne + ni * pic_info.mime)
vx = (ne * vex + ni * vix * pic_info.mime) * inrho
vy = (ne * vey + ni * viy * pic_info.mime) * inrho
vz = (ne * vez + ni * viz * pic_info.mime) * inrho
vdot_kappa = vx * kappax + vy * kappay + vz * kappaz
nbands = 7
nreduce = 16
nxr = pic_info.nx // nreduce
nzr = pic_info.nz // nreduce
ntot = np.zeros((nzr, nxr))
nhigh = np.zeros((nzr, nxr))
fig = plt.figure(figsize=[14, 7])
rect0 = [0.055, 0.75, 0.4, 0.20]
hgap, vgap = 0.09, 0.025
if species == 'e':
nmins = [1E-1, 1E-2, 5E-3, 1E-3, 6E-5, 2E-5, 6E-6]
nmaxs = [5E0, 1E0, 5E-1, 1E-1, 6E-3, 2E-3, 6E-4]
else:
nmins = [1E-1, 2E-2, 1E-2, 5E-3, 5E-4, 5E-5, 1.2E-5]
nmaxs = [5E0, 2E0, 1E0, 5E-1, 5E-2, 5E-3, 1.2E-3]
nmins = np.asarray(nmins) / 10
nmaxs = np.asarray(nmaxs) / 10
axs = []
rects = []
nrows = (nbands + 1) // 2
for iband in range(nbands + 1):
row = iband % nrows
col = iband // nrows
if row == 0:
rect = np.copy(rect0)
rect[0] += (rect[2] + hgap) * col
ax = fig.add_axes(rect)
ax.set_ylim([-20, 20])
ax.tick_params(bottom=True, top=True, left=True, right=True)
ax.tick_params(axis='x', which='minor', direction='in')
ax.tick_params(axis='x', which='major', direction='in')
ax.tick_params(axis='y', which='minor', direction='in')
ax.tick_params(axis='y', which='major', direction='in')
if row < nrows - 1:
ax.tick_params(axis='x', labelbottom=False)
else:
ax.set_xlabel(r'$x/d_i$', fontsize=16)
if col == 0:
ax.set_ylabel(r'$z/d_i$', fontsize=16)
ax.tick_params(labelsize=12)
axs.append(ax)
rects.append(np.copy(rect))
rect[1] -= rect[3] + vgap
band_break = 4
for iband in range(nbands):
print("Energy band: %d" % iband)
if iband < band_break:
ax = axs[iband]
rect = rects[iband]
else:
ax = axs[iband + 1]
rect = rects[iband + 1]
fname = (pic_run_dir + "data-smooth2/n" + species + "_" + str(iband) +
"_" + str(tindex) + ".gda")
nrho = np.fromfile(fname, dtype=np.float32)
nrho = nrho.reshape((nzr, nxr))
if iband >= 5:
nhigh += nrho
ntot += nrho
nmin, nmax = nmins[iband], nmaxs[iband]
p1 = ax.imshow(nrho + 1E-10,
extent=[xmin, xmax, zmin, zmax],
norm=LogNorm(vmin=nmin, vmax=nmax),
cmap=plt.cm.inferno,
aspect='auto',
origin='lower',
interpolation='bicubic')
# ax.contour(x, z, Ay, colors='w', linewidths=0.5)
if iband == 0:
label1 = r'$n(\varepsilon < 10\varepsilon_\text{th})$'
elif iband > 0 and iband < nbands - 1:
label1 = (r'$n(' + str(2**(iband - 1) * 10) +
r'\varepsilon_\text{th} < ' + r'\varepsilon < ' +
str(2**iband * 10) + r'\varepsilon_\text{th})$')
else:
label1 = (r'$n(\varepsilon > ' + str(2**(nbands - 2) * 10) +
r'\varepsilon_\text{th})$')
ax.text(0.98,
0.87,
label1,
color='k',
fontsize=16,
bbox=dict(facecolor='w',
alpha=0.75,
edgecolor='none',
boxstyle="round,pad=0.1"),
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
twci = math.ceil((tframe * pic_info.dt_fields) / 0.1) * 0.1
text1 = r'$t\Omega_{ci}=' + ("{%0.0f}" % twci) + '$'
if iband == 0:
# ax.set_title(text1, fontsize=16)
xpos = (rect[2] + hgap * 0.5) / rect[2]
ax.text(xpos,
1.1,
text1,
color='k',
fontsize=16,
bbox=dict(facecolor='w',
alpha=0.75,
edgecolor='none',
boxstyle="round,pad=0.1"),
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes)
rect_cbar = np.copy(rect)
rect_cbar[0] += rect[2] + 0.01
rect_cbar[2] = 0.007
cbar_ax = fig.add_axes(rect_cbar)
cbar = fig.colorbar(p1, cax=cbar_ax, extend='both')
cbar.ax.tick_params(labelsize=12)
ax = axs[band_break]
rect = rects[band_break]
vmin, vmax = -1.0, 1.0
knorm = 100
p1 = ax.imshow(vdot_kappa * knorm,
extent=[xmin, xmax, zmin, zmax],
vmin=vmin,
vmax=vmax,
cmap=plt.cm.seismic,
aspect='auto',
origin='lower',
interpolation='bicubic')
# ax.contour(x, z, Ay, colors='k', linewidths=0.5)
ax.tick_params(labelsize=12)
label1 = r'$' + str(knorm) + r'\boldsymbol{v}\cdot\boldsymbol{\kappa}$'
ax.text(0.98,
0.87,
label1,
color='w',
fontsize=16,
bbox=dict(facecolor='k',
alpha=0.5,
edgecolor='none',
boxstyle="round,pad=0.1"),
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
rect_cbar = np.copy(rect)
rect_cbar[0] += rect[2] + 0.01
rect_cbar[2] = 0.007
cbar_ax = fig.add_axes(rect_cbar)
cbar = fig.colorbar(p1, cax=cbar_ax, extend='both')
cbar.set_ticks(np.linspace(-1, 1, num=5))
cbar.ax.tick_params(labelsize=12)
fdir = '../img/open_bc/rho_bands_2d/' + pic_run + '/'
mkdir_p(fdir)
fname = (fdir + 'nrho_bands_' + species + '_' + str(tframe) + ".jpg")
fig.savefig(fname, dpi=200)
if show_plot:
plt.show()
else:
plt.close()
def transfer_pic_to_mhd(run_dir, run_name, tframe):
"""Transfer the required fields
Args:
run_dir: simulation directory
run_name: name of the simulation run
tframe: time frame
boundary_x: boundary condition along x-direction
0 for periodic; 1 for others
boundary_z: boundary condition along z-direction
"""
print("Time frame: %d" % tframe)
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
lx_di = pic_info.lx_di
lz_di = pic_info.lz_di
kwargs = {"current_time": tframe, "xl": 0, "xr": lx_di,
"zb": -0.5 * lz_di, "zt": 0.5 * lz_di}
fname = run_dir + "data/bx.gda"
x, z, bx = read_2d_fields(pic_info, fname, **kwargs)
fname = run_dir + "data/by.gda"
x, z, by = read_2d_fields(pic_info, fname, **kwargs)
fname = run_dir + "data/bz.gda"
x, z, bz = read_2d_fields(pic_info, fname, **kwargs)
nx, = x.shape
nz, = z.shape
mhd_data = np.zeros((nz+4, nx+4, 8), dtype=np.float32)
# We need to switch y and z directions
absB = np.sqrt(bx**2 + by**2 + bz**2)
mhd_data[2:nz+2, 2:nx+2, 4] = bx
mhd_data[2:nz+2, 2:nx+2, 5] = bz
mhd_data[2:nz+2, 2:nx+2, 6] = -by
mhd_data[2:nz+2, 2:nx+2, 7] = absB
del bx, by, bz, absB
mime = pic_info.mime
# Electron
fname = run_dir + "data/ne.gda"
x, z, ne = read_2d_fields(pic_info, fname, **kwargs)
fname = run_dir + "data/vex.gda"
x, z, vex = read_2d_fields(pic_info, fname, **kwargs)
fname = run_dir + "data/vey.gda"
x, z, vey = read_2d_fields(pic_info, fname, **kwargs)
fname = run_dir + "data/vez.gda"
x, z, vez = read_2d_fields(pic_info, fname, **kwargs)
vx = ne * vex
vy = ne * vey
vz = ne * vez
del vex, vey, vez
# Ion
fname = run_dir + "data/ni.gda"
x, z, ni = read_2d_fields(pic_info, fname, **kwargs)
fname = run_dir + "data/vix.gda"
x, z, vix = read_2d_fields(pic_info, fname, **kwargs)
fname = run_dir + "data/viy.gda"
x, z, viy = read_2d_fields(pic_info, fname, **kwargs)
fname = run_dir + "data/viz.gda"
x, z, viz = read_2d_fields(pic_info, fname, **kwargs)
imass = 1.0 / (ne + ni * mime)
vx = (vx + ni * mime * vix) * imass
vy = (vy + ni * mime * viy) * imass
vz = (vz + ni * mime * viz) * imass
del vix, viy, viz, ne, ni, imass
absV = np.sqrt(vx**2 + vy**2 + vz**2)
mhd_data[2:nz+2, 2:nx+2, 0] = vx
mhd_data[2:nz+2, 2:nx+2, 1] = vz
mhd_data[2:nz+2, 2:nx+2, 2] = -vy
mhd_data[2:nz+2, 2:nx+2, 3] = absV
del vx, vy, vz, absV
# Assuming periodic boundary along x for fields and particles
# Assuming conducting boundary along z for fields and reflective for particles
mhd_data[:, 0:2, :] = mhd_data[:, nx-1:nx+1, :]
mhd_data[:, nx+2:, :] = mhd_data[:, 3:5, :]
mhd_data[0:2, :, :] = mhd_data[3:1:-1, :, :]
mhd_data[nz+2:, :, :] = mhd_data[nz+1:nz-1:-1, :, :]
fpath = run_dir + 'bin_data/'
mkdir_p(fpath)
fname = fpath + 'mhd_data_' + str(tframe).zfill(4)
print(mhd_data.shape)
print(np.isfortran(mhd_data))
mhd_data.tofile(fname)
def compare_spectrum(plot_config):
"""Compare 2D and 3D spectra
Args:
plot_config: plotting configuration
"""
species = plot_config["species"]
bg = plot_config["bg"]
root_dir = "/net/scratch3/xiaocanli/reconnection/Cori_runs/"
pic_runs = ["2D-sigmae100-Lx125-bg0.0-100ppc-15gr"]
pic_runs.append("3D-sigmae100-Lx125-bg0.0-100ppc-1024KNL")
tframes = np.asarray([0, 20, 40, 52])
fig = plt.figure(figsize=[7, 5])
rect = [0.14, 0.14, 0.8, 0.8]
ax = fig.add_axes(rect)
# colors = np.copy(COLORS)
# colors[5] = colors[6]
colors = palettable.tableau.Tableau_10.mpl_colors
for irun, pic_run in enumerate(pic_runs):
pic_run_dir = root_dir + pic_run + "/"
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
if species == 'e':
vth = pic_info.vthe
else:
vth = pic_info.vthi
ebins = np.logspace(-4, 6, 1000)
if species in ['i', 'ion', 'proton']:
ebins *= pic_info.mime
if irun == 0:
fnorm = 1
lstyle = '--'
else:
fnorm = pic_info.ny
lstyle = '-'
tinterval = pic_info.eparticle_interval
dtf = math.ceil(pic_info.dt_fields / 0.1) * 0.1
pdim = "2D" if "2D" in pic_run else "3D"
for iframe, tframe in enumerate(tframes):
tindex = tinterval * tframe
fname = (pic_run_dir + "spectrum_combined/spectrum_" + species +
"_" + str(tindex) + ".dat")
spect = np.fromfile(fname, dtype=np.float32)
ndata, = spect.shape
spect[3:] /= np.gradient(ebins) * fnorm
if iframe == len(tframes) - 1:
eindex_20, ene = find_nearest(ebins, 20)
spect[spect == 0] = np.nan
if iframe > 0:
ax.loglog(ebins,
spect[3:],
linewidth=1,
linestyle=lstyle,
color=colors[iframe - 1])
else:
if irun == 1:
ax.loglog(ebins,
spect[3:],
linewidth=1,
linestyle='--',
color='k')
if species == 'e':
fpower = 1E10 * ebins**-2.5
power_index = "{%0.1f}" % -2.5
pname = r'$\sim \varepsilon^{' + power_index + '}$'
ax.loglog(ebins[548:648], fpower[548:648], linewidth=1, color='k')
else:
fpower = 2E12 * ebins**-3.5
power_index = "{%0.1f}" % -3.5
pname = r'$\sim \varepsilon^{' + power_index + '}$'
ax.loglog(ebins[368:468], fpower[368:468], linewidth=1, color='k')
ax.text(0.95,
0.7,
pname,
color='k',
fontsize=20,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax.plot([0, 10], [0, 0],
linestyle="--",
color='k',
linewidth=1,
label='2D')
ax.plot([0, 10], [0, 0], linestyle="-", color='k', linewidth=1, label='3D')
ax.legend(loc=3,
prop={'size': 20},
ncol=1,
shadow=False,
fancybox=False,
frameon=False)
ax.tick_params(bottom=True, top=True, left=True, right=True)
ax.tick_params(axis='x', which='minor', direction='in', top=True)
ax.tick_params(axis='x', which='major', direction='in')
ax.tick_params(axis='y', which='minor', direction='in')
ax.tick_params(axis='y', which='major', direction='in')
if species in ['e', 'electron']:
ax.set_xlim([1E-1, 2E3])
else:
ax.set_xlim([1E-1, 2E3])
ax.set_ylim([1E-5, 1E9])
text1 = r'$(\gamma - 1)m_' + species + r'c^2$'
ax.set_xlabel(text1, fontsize=20)
ax.set_ylabel(r'$f(\gamma - 1)$', fontsize=20)
ax.tick_params(labelsize=16)
if species == 'e':
xpos = [0.4, 0.7, 0.87, 0.94]
else:
xpos = [0.4, 0.67, 0.86, 0.93]
text1 = r'$t\Omega_{ci}=0$'
ax.text(xpos[0],
0.10,
text1,
color='k',
fontsize=16,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='center',
verticalalignment='bottom',
transform=ax.transAxes)
text2 = r'$' + str(int(tframes[1] * 10)) + '$'
ax.text(xpos[1],
0.10,
text2,
color=colors[0],
fontsize=16,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='center',
verticalalignment='bottom',
transform=ax.transAxes)
text3 = r'$' + str(int(tframes[2] * 10)) + '$'
ax.text(xpos[2],
0.10,
text3,
color=colors[1],
fontsize=16,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='center',
verticalalignment='bottom',
transform=ax.transAxes)
text4 = r'$' + str(int(tframes[3] * 10)) + '$'
ax.text(xpos[3],
0.10,
text4,
color=colors[2],
fontsize=16,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='bottom',
transform=ax.transAxes)
fdir = '../img/cori_sigma/spectrum/'
mkdir_p(fdir)
fname = fdir + species + 'spect_32.pdf'
fig.savefig(fname)
plt.show()
def plot_temp_dist(plot_config, show_plot=True):
"""
Plot temperature distribution
"""
pic_run = plot_config["pic_run"]
pic_run_dir = plot_config["pic_run_dir"]
species = plot_config["species"]
tframe = plot_config["tframe"]
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
tinit = pic_info.vthe**2
fdir = '../data/temperature_anisotropy/' + pic_run + '/'
fname = fdir + 'ftpara_' + species + '_' + str(tframe) + '.gda'
fdata = np.fromfile(fname, dtype=np.float32)
nbins_temp = int(fdata[0])
temp_bins_edge = fdata[1:nbins_temp + 2]
temp_bins_mid = 0.5 * (temp_bins_edge[1:] + temp_bins_edge[:-1])
temp_bins_mid /= tinit
dtemp = np.diff(temp_bins_edge)
ftpara = fdata[nbins_temp + 2:] / dtemp
fname = fdir + 'ftperp_' + species + '_' + str(tframe) + '.gda'
fdata = np.fromfile(fname, dtype=np.float32)
ftperp = fdata[nbins_temp + 2:] / dtemp
fig = plt.figure(figsize=[3.25, 2.5])
rect = [0.16, 0.16, 0.79, 0.8]
ax = fig.add_axes(rect)
ax.set_prop_cycle('color', COLORS)
ax.tick_params(bottom=True, top=True, left=True, right=True)
ax.tick_params(axis='x',
which='minor',
direction='in',
bottom=True,
top=True)
ax.tick_params(axis='x',
which='major',
direction='in',
bottom=True,
top=True)
ax.tick_params(axis='y', which='minor', direction='in')
ax.tick_params(axis='y', which='major', direction='in')
ax.loglog(temp_bins_mid, ftpara, linewidth=1, label=r'$T_\parallel$')
ax.loglog(temp_bins_mid, ftperp, linewidth=1, label=r'$T_\perp$')
ax.legend(loc=1,
prop={'size': 10},
ncol=1,
shadow=False,
fancybox=False,
frameon=False)
ax.set_xlim([1E-1, 1E2])
ax.tick_params(labelsize=8)
ax.set_xlabel(r'$T/T_0$', fontsize=10)
ax.set_ylabel(r'$f(T)$', fontsize=10)
fdir = '../img/temp_dist/' + pic_run + '/'
mkdir_p(fdir)
fname = fdir + 'temp_dist_' + species + '_' + str(tframe) + '.pdf'
fig.savefig(fname)
if show_plot:
plt.show()
else:
plt.close()
help='particle species')
parser.add_argument('--run_dir', action="store", default=default_run_dir,
help='run directory')
parser.add_argument('--run_name', action="store", default=default_run_name,
help='run name')
parser.add_argument('--tframe_fields', action="store", default='30', type=int,
help='Time frame for fields')
parser.add_argument('--multi_frames', action="store_true", default=False,
help='whether analyzing multiple frames')
return parser.parse_args()
if __name__ == "__main__":
args = get_cmd_args()
run_name = args.run_name
run_dir = args.run_dir
species = args.species
tframe_fields = args.tframe_fields
multi_frames = args.multi_frames
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
save_mhd_config(run_name)
# transfer_pic_to_mhd(run_dir, run_name, 0)
ncores = multiprocessing.cpu_count()
ncores = 10
cts = range(pic_info.ntf)
def processInput(job_id):
time_frame = job_id
transfer_pic_to_mhd(run_dir, run_name, time_frame)
Parallel(n_jobs=ncores)(delayed(processInput)(ct) for ct in cts)
def save_mhd_config(run_name):
"""Save MHD configuration
Need to switch y and z directions
Args:
run_name: simulation run name
"""
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
double_data = np.zeros(13)
int_data = np.zeros(14, dtype=np.int32)
smime = math.sqrt(pic_info.mime)
lx = pic_info.lx_di * smime
ly = pic_info.lz_di * smime
lz = 0.0
nx = pic_info.nx
ny = pic_info.nz
nz = pic_info.ny
if nx > 1:
double_data[0] = lx / nx
else:
double_data[0] = 0.0
if ny > 1:
double_data[1] = ly / ny
else:
double_data[1] = 0.0
if nz > 1:
double_data[2] = lz / nz
else:
double_data[2] = 0.0
double_data[3] = 0.0
double_data[4] = -0.5 * ly
double_data[5] = 0.0
double_data[6] = lx
double_data[7] = 0.5 * ly
double_data[8] = 0.0
double_data[9] = lx
double_data[10] = ly
double_data[11] = lz
double_data[12] = pic_info.dtwpe * pic_info.fields_interval
int_data[0] = pic_info.nx
int_data[1] = pic_info.nz
int_data[2] = pic_info.ny
int_data[3] = pic_info.nx / pic_info.topology_x
int_data[4] = pic_info.nz / pic_info.topology_z
int_data[5] = pic_info.ny / pic_info.topology_y
int_data[6] = pic_info.topology_x
int_data[7] = pic_info.topology_z
int_data[8] = pic_info.topology_y
int_data[9] = 9
int_data[10] = 0 # Periodic boundary condition as default
int_data[11] = 0
int_data[12] = 0
int_data[13] = 0
fpath = run_dir + 'bin_data/'
mkdir_p(fpath)
fname = fpath + 'mhd_config.dat'
double_data.tofile(fname)
f = open(fname, 'a')
int_data.tofile(f)
f.close()
def contour_tratio_beta(plot_config, show_plot=True):
"""
Plot contour of temperature ratio and parallel plasma beta
"""
pic_run = plot_config["pic_run"]
pic_run_dir = plot_config["pic_run_dir"]
species = plot_config["species"]
tframe = plot_config["tframe"]
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
fdir = '../data/temperature_anisotropy/' + pic_run + '/'
fname = fdir + 'ftratio_bpara_' + species + '_' + str(tframe) + '.gda'
fdata = np.fromfile(fname, dtype=np.float32)
nbins_tratio = int(fdata[0])
nbins_beta = int(fdata[1])
tratio_bins_edge = fdata[2:nbins_tratio+3]
beta_bins_edge = fdata[nbins_tratio+3:nbins_beta+nbins_tratio+4]
tratio_bins_mid = 0.5 * (tratio_bins_edge[1:] + tratio_bins_edge[:-1])
dtratio = np.diff(tratio_bins_edge)
beta_bins_mid = 0.5 * (beta_bins_edge[1:] + beta_bins_edge[:-1])
dbeta = np.diff(beta_bins_edge)
ftratio_beta = fdata[nbins_beta+nbins_tratio+4:]
ftratio_beta = ftratio_beta.reshape([nbins_beta, nbins_tratio]).T
delta = np.dot(dtratio[:, None], dbeta[None, :])
ftratio_beta /= delta
# ftratio_beta[ftratio_beta <= 0] = np.nan
tratio_min = tratio_bins_mid[0]
tratio_max = tratio_bins_mid[-1]
beta_min = beta_bins_mid[0]
beta_max = beta_bins_mid[-1]
# According to Bale et al. 2009,
# T_\perp / T_\parallel = 1 + a/(\beta_\parallel - \beta_0)^b.
# For mirror instability threshold, (a, b, \beta_0) = (0.77, 0.76, -0.016)
# For oblique firehose instability threshold, (a, b, \beta_0) = (-1.4, 1.0, -0.11)
tratio_firhose = 1 - 1.4 / (beta_bins_mid + 0.11)
tratio_mirror = 1 + 0.77 / (beta_bins_mid + 0.016)**0.76
tratio_firhose[tratio_firhose < 0] = np.nan
fig = plt.figure(figsize=[3.25, 2.5])
rect = [0.18, 0.16, 0.67, 0.8]
ax = fig.add_axes(rect)
ax.tick_params(bottom=True, top=True, left=True, right=True)
ax.tick_params(axis='x', which='minor', direction='in', bottom=True, top=True)
ax.tick_params(axis='x', which='major', direction='in', bottom=True, top=True)
ax.tick_params(axis='y', which='minor', direction='in')
ax.tick_params(axis='y', which='major', direction='in')
im = ax.pcolormesh(beta_bins_mid, tratio_bins_mid, ftratio_beta,
cmap=plt.cm.jet, norm=LogNorm(vmin=1E0, vmax=1E12))
ax.set_xscale('log')
ax.set_yscale('log')
ax.plot(beta_bins_mid, tratio_firhose, color='k', linestyle='--')
ax.plot(beta_bins_mid, tratio_mirror, color='k', linestyle='--')
ax.set_xlim([1E-3, 1E3])
ax.set_ylim([1E-2, 1E2])
ax.tick_params(labelsize=8)
ax.set_xlabel(r'$\beta_\parallel$', fontsize=10)
ax.set_ylabel(r'$T_\perp/T_\parallel$', fontsize=10)
rect_cbar = np.copy(rect)
rect_cbar[0] += rect[2] + 0.02
rect_cbar[2] = 0.02
cbar_ax = fig.add_axes(rect_cbar)
cbar = fig.colorbar(im, cax=cbar_ax)
cbar.ax.tick_params(labelsize=8)
fdir = '../img/brazil_plot/' + pic_run + '/'
mkdir_p(fdir)
fname = fdir + 'tratio_bpara_' + species + '_' + str(tframe) + '.jpg'
fig.savefig(fname, dpi=300)
if show_plot:
plt.show()
else:
plt.close()
def energy_conversion(plot_config):
"""Plot energy conversion
Args:
plot_config: plotting configuration
"""
pic_run = plot_config["pic_run"]
pic_run_dir = plot_config["pic_run_dir"]
fig = plt.figure(figsize=[3.25, 2.5])
w1, h1 = 0.78, 0.78
xs, ys = 0.96 - w1, 0.96 - h1
ax = fig.add_axes([xs, ys, w1, h1])
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
tenergy = pic_info.tenergy
enorm = pic_info.ene_magnetic[0]
ene_bx = pic_info.ene_bx
ene_by = pic_info.ene_by
ene_bz = pic_info.ene_bz
ene_magnetic = pic_info.ene_magnetic
ene_electric = pic_info.ene_electric
kene_e = pic_info.kene_e
kene_i = pic_info.kene_i
etot = ene_magnetic + ene_electric + kene_e + kene_i
print("Energy conservation: %e" % ((etot[-1] - etot[0]) / etot[0]))
print("Energy conversion: %e" %
((ene_magnetic[-1] - ene_magnetic[0]) / ene_magnetic[0]))
ene_bx /= enorm
ene_by /= enorm
ene_bz /= enorm
ene_magnetic /= enorm
kene_e /= enorm
kene_i /= enorm
ax.plot(tenergy,
ene_magnetic,
linewidth=1,
linestyle='-',
label='magnetic')
ax.plot(tenergy, kene_e, linewidth=1, linestyle='-', label='electron')
ax.plot(tenergy, kene_i, linewidth=1, linestyle='-', label='ion')
ax.legend(loc=6,
prop={'size': 12},
ncol=1,
shadow=False,
fancybox=False,
frameon=False)
ax.tick_params(bottom=True, top=False, left=True, right=True)
ax.set_xlim([0, 400])
ax.set_ylim([0, 1.05])
ax.set_xlabel(r'$t\Omega_{ci}$', fontsize=12)
ax.set_ylabel(r'$\text{Energy}/\varepsilon_{B0}$', fontsize=12)
ax.tick_params(labelsize=10)
fdir = '../img/' + pic_run + '/'
mkdir_p(fdir)
fname = fdir + 'econv.pdf'
fig.savefig(fname)
plt.show()
def particle_energization(plot_config, show_plot=True):
"""Particle-based energization
Args:
plot_config: plotting configuration
"""
species = plot_config["species"]
tframe = plot_config["tframe"]
pic_run = plot_config["pic_run"]
pic_run_dir = plot_config["pic_run_dir"]
ylims, data_indices = get_plot_setup(plot_config["plot_type"], species)
tstarts = [6, 10, 20, 30]
tends = [10, 20, 30, 40]
nplots = len(tstarts)
fnorm = 1E-3
for iplot in range(nplots):
tstart = tstarts[iplot]
tend = tends[iplot]
ylim = np.asarray(ylims[iplot]) / fnorm
fig1 = plt.figure(figsize=[4.8, 2.0])
box1 = [0.14, 0.2, 0.8, 0.75]
axs1 = []
nframes = tend - tstart
fpath = "../data/particle_interp/" + pic_run + "/"
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
dtp = math.ceil(pic_info.dt_particles / 0.1) * 0.1
ax = fig1.add_axes(box1)
for tframe in range(tstart, tend + 1):
tstep = tframe * pic_info.particle_interval
tframe_fluid = tstep // pic_info.fields_interval
fname = fpath + "particle_energization_" + species + "_" + str(
tstep) + ".gda"
fdata = np.fromfile(fname, dtype=np.float32)
nbins = int(fdata[0])
nbinx = int(fdata[1])
nvar = int(fdata[2])
ebins = fdata[3:nbins + 3]
fbins = np.sum(fdata[nbins + 3:].reshape((nvar, nbinx, nbins)),
axis=1)
if species == 'i':
ebins *= pic_info.mime # ebins are actually gamma
fbins[1:, :] = div0(fbins[1:, :], fbins[0, :])
# normalized with thermal energy
if species == 'e':
vth = pic_info.vthe
else:
vth = pic_info.vthi
gama = 1.0 / math.sqrt(1.0 - 3 * vth**2)
eth = gama - 1.0
if species == 'i':
eth *= pic_info.mime
ebins /= eth
color = plt.cm.Spectral_r((tframe - tstart) / float(nframes), 1)
fdata = np.zeros(nbins)
for idata in data_indices:
fdata += fbins[idata, :]
ax.semilogx(ebins, fdata / fnorm, linewidth=1, color=color)
if species == 'e':
if "2D" in pic_run:
ax.set_xlim([1E0, 200])
else:
ax.set_xlim([1E0, 200])
else:
if "2D" in pic_run:
ax.set_xlim([1E0, 500])
else:
ax.set_xlim([1E0, 1000])
ax.set_ylim(ylim)
box1[0] += box1[2] + 0.02
ax.set_ylabel(r'$\left<\nu^\text{I}_\text{COM}\right>/10^{-3}$',
fontsize=10)
ax.set_xlabel(r'$\varepsilon/\varepsilon_\text{th}$', fontsize=10)
ax.tick_params(labelsize=8)
ax.plot(ax.get_xlim(), [0, 0], linestyle='--', color='k')
ax.tick_params(bottom=True, top=False, left=True, right=False)
ax.tick_params(axis='x', which='minor', direction='in')
ax.tick_params(axis='x', which='major', direction='in')
ax.tick_params(axis='y', which='minor', direction='in')
ax.tick_params(axis='y', which='major', direction='in')
box1[0] -= box1[2] + 0.02
rect_cbar = np.copy(box1)
rect_cbar[0] += box1[2] + 0.01
rect_cbar[2] = 0.015
cax = fig1.add_axes(rect_cbar)
cax.tick_params(axis='y', which='major', direction='in')
sm = plt.cm.ScalarMappable(cmap=plt.cm.Spectral_r,
norm=plt.Normalize(vmin=tstart * dtp,
vmax=tend * dtp))
# fake up the array of the scalar mappable. Urgh...
sm._A = []
cbar = fig1.colorbar(sm, cax=cax)
cbar.set_label(r'$t\Omega_{ci}$', fontsize=12)
cbar.set_ticks((np.linspace(tstart, tend, tend - tstart + 1) * dtp))
cbar.ax.tick_params(labelsize=10)
# bg_str = str(int(bg * 10)).zfill(2)
# fdir = '../img/cori_3d/particle_energization/bg' + bg_str + '/'
# mkdir_p(fdir)
# fname = (fdir + 'particle_' + plot_config["plot_type"] + '_' +
# species + '_' + str(iplot) + '.pdf')
# fig1.savefig(fname)
if show_plot:
plt.show()
else:
plt.close('all')
def fluid_energization(plot_config, show_plot=True):
"""Plot fluid energization
Args:
plot_config: plotting configuration
"""
species = plot_config["species"]
pic_run = plot_config["pic_run"]
pic_run_dir = plot_config["pic_run_dir"]
if species == 'e':
ylim = [-0.2, 1.0]
else:
ylim = [-0.6, 1.7]
fig1 = plt.figure(figsize=[7, 2.5])
box1 = [0.09, 0.18, 0.85, 0.68]
axs1 = []
fig2 = plt.figure(figsize=[7, 2.5])
box2 = [0.09, 0.18, 0.85, 0.68]
axs2 = []
fig3 = plt.figure(figsize=[7, 2.5])
box3 = [0.09, 0.18, 0.85, 0.68]
axs3 = []
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
if "3D" in pic_run:
enorm = pic_info.ny
else:
enorm = 1.0
tfields = pic_info.tfields
tenergy = pic_info.tenergy
fname = "../data/fluid_energization/" + pic_run + "/"
fname += "emf_ptensor_" + species + '.gda'
fluid_ene = np.fromfile(fname, dtype=np.float32)
nvar = int(fluid_ene[0])
nframes = int(fluid_ene[1])
fluid_ene[2:] /= enorm
curv_drift_dote = fluid_ene[2:nframes + 2]
bulk_curv_dote = fluid_ene[nframes + 2:2 * nframes + 2]
grad_drift_dote = fluid_ene[2 * nframes + 2:3 * nframes + 2]
magnetization_dote = fluid_ene[3 * nframes + 2:4 * nframes + 2]
comp_ene = fluid_ene[4 * nframes + 2:5 * nframes + 2]
shear_ene = fluid_ene[5 * nframes + 2:6 * nframes + 2]
ptensor_ene = fluid_ene[6 * nframes + 2:7 * nframes + 2]
pgyro_ene = fluid_ene[7 * nframes + 2:8 * nframes + 2]
fname = "../data/fluid_energization/" + pic_run + "/"
fname += "para_perp_acc_" + species + '.gda'
fluid_ene = np.fromfile(fname, dtype=np.float32)
nvar = int(fluid_ene[0])
nframes = int(fluid_ene[1])
fluid_ene[2:] /= enorm
acc_drift_dote_t = fluid_ene[2:nframes + 2]
acc_drift_dote_s = fluid_ene[nframes + 2:2 * nframes + 2]
acc_drift_dote = acc_drift_dote_t + acc_drift_dote_s
epara_ene = fluid_ene[4 * nframes + 2:5 * nframes + 2]
eperp_ene = fluid_ene[5 * nframes + 2:6 * nframes + 2]
acc_drift_dote[-1] = acc_drift_dote[-2]
jperp_dote = curv_drift_dote + grad_drift_dote + magnetization_dote
jagy_dote = ptensor_ene - jperp_dote
if species == 'e':
dkene = pic_info.dkene_e
else:
dkene = pic_info.dkene_i
dkene /= enorm
ax = fig1.add_axes(box1)
axs1.append(ax)
COLORS = palettable.tableau.Tableau_10.mpl_colors
ax.set_prop_cycle('color', COLORS)
label2 = (r'$\boldsymbol{j}_{' + species + '\perp}' +
r'\cdot\boldsymbol{E}_\perp$')
ax.plot(tfields, eperp_ene, linewidth=1, label=label2)
label1 = (r'$\boldsymbol{j}_{' + species + '\parallel}' +
r'\cdot\boldsymbol{E}_\parallel$')
ax.plot(tfields, epara_ene, linewidth=1, label=label1)
label6 = r'$dK_' + species + '/dt$'
ax.plot(tenergy, dkene, linewidth=1, label=label6)
label3 = r'$(\nabla\cdot\tensorsym{P}_' + species + r')\cdot\boldsymbol{v}_E$'
ax.plot(tfields, ptensor_ene, linewidth=1, label=label3)
ax.plot([0, tenergy.max()], [0, 0], color='k', linestyle='--')
ax.set_xlim([0, np.max(tfields)])
# ax.set_ylim(ylim)
ax.tick_params(labelsize=10)
ax.set_ylabel('Energization', fontsize=12)
pdim = "2D" if "2D" in pic_run else "3D"
ax.text(0.02,
0.9,
pdim,
color='k',
fontsize=12,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
ax.set_xlabel(r'$t\Omega_{ci}$', fontsize=12)
ax = fig2.add_axes(box1)
axs2.append(ax)
# COLORS = palettable.colorbrewer.qualitative.Set1_9.mpl_colors
ax.set_prop_cycle('color', COLORS)
label2 = (r'$\boldsymbol{j}_{' + species + '\perp}' +
r'\cdot\boldsymbol{E}_\perp$')
ax.plot(tfields, eperp_ene, linewidth=1, label=label2)
ax.plot(tfields, curv_drift_dote, linewidth=1, label='Curvature')
# ax.plot(tfields, bulk_curv_dote, linewidth=1, label='Bulk Curvature')
ax.plot(tfields, grad_drift_dote, linewidth=1, label='Gradient')
ax.plot(tfields, magnetization_dote, linewidth=1, label='Magnetization')
# ax.plot(tfields, acc_drift_dote, linewidth=1, label='Polarization')
jdote_sum = (curv_drift_dote + grad_drift_dote + magnetization_dote +
jagy_dote + acc_drift_dote)
# ax.plot(tfields, jdote_sum, linewidth=1)
ax.plot([0, tenergy.max()], [0, 0], color='k', linestyle='--')
ax.set_xlim([0, np.max(tfields)])
# ax.set_ylim(ylim)
ax.tick_params(labelsize=10)
ax.set_ylabel('Energization', fontsize=12)
pdim = "2D" if "2D" in pic_run else "3D"
ax.text(0.02,
0.9,
pdim,
color='k',
fontsize=12,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
ax.set_xlabel(r'$t\Omega_{ci}$', fontsize=12)
ax = fig3.add_axes(box1)
axs3.append(ax)
# COLORS = palettable.colorbrewer.qualitative.Set1_9.mpl_colors
ax.set_prop_cycle('color', COLORS)
label2 = (r'$\boldsymbol{j}_{' + species + '\perp}' +
r'\cdot\boldsymbol{E}_\perp' + '$')
ax.plot(tfields, eperp_ene, linewidth=1, label=label2)
ax.plot(tfields, comp_ene, linewidth=1, label='Compression')
ax.plot(tfields, shear_ene, linewidth=1, label='Shear')
# label2 = (r'$\boldsymbol{j}_{' + species + '\perp}' +
# r'\cdot\boldsymbol{E}_\perp -' + 'n_' + species +
# 'm_' + species + r'(d\boldsymbol{u}_' + species +
# r'/dt)\cdot\boldsymbol{v}_E$')
# ax.plot(tfields, eperp_ene - acc_drift_dote, linewidth=1, label=label2)
label4 = (r'$\boldsymbol{j}_{' + species + r'-\text{agy}}' +
r'\cdot\boldsymbol{E}_\perp$')
ax.plot(tfields, jagy_dote, linewidth=1, label=label4)
# jdote_sum = comp_ene + shear_ene + jagy_dote
# ax.plot(tfields, jdote_sum, linewidth=1)
ax.plot([0, tenergy.max()], [0, 0], color='k', linestyle='--')
ax.set_xlim([0, np.max(tfields)])
# ax.set_ylim(ylim)
ax.tick_params(labelsize=10)
ax.set_ylabel('Energization', fontsize=12)
pdim = "2D" if "2D" in pic_run else "3D"
ax.text(0.02,
0.9,
pdim,
color='k',
fontsize=12,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
ax.set_xlabel(r'$t\Omega_{ci}$', fontsize=12)
box1[0] += box1[2] + 0.07
axs1[0].legend(loc='upper left',
prop={'size': 12},
ncol=4,
bbox_to_anchor=(0, 1.22),
shadow=False,
fancybox=False,
frameon=False)
axs2[0].legend(loc='upper left',
prop={'size': 12},
ncol=4,
bbox_to_anchor=(0, 1.22),
shadow=False,
fancybox=False,
frameon=False)
axs3[0].legend(loc='upper left',
prop={'size': 12},
ncol=4,
bbox_to_anchor=(0, 1.22),
shadow=False,
fancybox=False,
frameon=False)
fdir = '../img/cori_3d/fluid_energization/'
mkdir_p(fdir)
fname = fdir + 'fluid_ene_' + species + '.pdf'
# fig1.savefig(fname)
fdir = '../img/cori_3d/fluid_energization/'
mkdir_p(fdir)
fname = fdir + 'fluid_drift_' + species + '.pdf'
# fig2.savefig(fname)
fdir = '../img/cori_3d/fluid_energization/'
mkdir_p(fdir)
fname = fdir + 'fluid_comp_shear_' + species + '.pdf'
# fig3.savefig(fname)
if show_plot:
plt.show()
else:
plt.close('all')
def energy_conversion(plot_config):
"""Plot energy conversion
Args:
plot_config: plotting configuration
"""
pic_run = plot_config["pic_run"]
root_dir = "/net/scratch3/xiaocanli/reconnection/Cori_runs/"
pic_run_dir = root_dir + pic_run + '/'
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
fig = plt.figure(figsize=[3.25, 2.5])
w1, h1 = 0.78, 0.78
xs, ys = 0.96 - w1, 0.96 - h1
ax = fig.add_axes([xs, ys, w1, h1])
COLORS = palettable.tableau.Tableau_10.mpl_colors
ax.set_prop_cycle('color', COLORS)
tenergy = pic_info.tenergy
enorm = pic_info.ene_magnetic[0]
ene_bx = pic_info.ene_bx
ene_by = pic_info.ene_by
ene_bz = pic_info.ene_bz
ene_magnetic = pic_info.ene_magnetic
ene_electric = pic_info.ene_electric
kene_e = pic_info.kene_e
kene_i = pic_info.kene_i
etot = ene_magnetic + ene_electric + kene_e + kene_i
dene_mag = ene_magnetic[-1] - ene_magnetic[0]
dene_ele = ene_electric[-1] - ene_electric[0]
print("Energy conservation: %e" % ((etot[-1] - etot[0]) / etot[0]))
print("Energy conversion: %e" % (dene_mag / ene_magnetic[0]))
print("Electron gain: %e" % ((kene_e[-1] - kene_e[0]) / abs(dene_mag)))
print("Ion gain: %e" % ((kene_i[-1] - kene_i[0]) / abs(dene_mag)))
print("Electric: %e" % (dene_ele / abs(dene_mag)))
ene_bx /= enorm
ene_by /= enorm
ene_bz /= enorm
ene_magnetic /= enorm
kene_e /= enorm
kene_i /= enorm
ax.plot(tenergy, ene_magnetic, linewidth=1, linestyle='-')
ax.plot(tenergy, kene_e, linewidth=1, linestyle='-')
ax.plot(tenergy, kene_i, linewidth=1, linestyle='-')
ax.text(0.03,
0.6,
"magnetic",
color=COLORS[0],
fontsize=12,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
ax.text(0.03,
0.5,
"electron",
color=COLORS[1],
fontsize=12,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
ax.text(0.03,
0.4,
"ion",
color=COLORS[2],
fontsize=12,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
ax.tick_params(bottom=True, top=True, left=True, right=True)
ax.tick_params(axis='x', which='minor', direction='in', top=False)
ax.tick_params(axis='x', which='major', direction='in')
ax.tick_params(axis='y', which='minor', direction='in', left=False)
ax.tick_params(axis='y', which='major', direction='in')
ax.set_xlim([0, 260])
ax.set_ylim([0, 1.05])
ax.set_xlabel(r'$t\Omega_{ci}$', fontsize=12)
ax.set_ylabel(r'$\text{Energy}/\varepsilon_{B0}$', fontsize=12)
ax.tick_params(labelsize=10)
fdir = '../img/cori_3d/' + pic_run + '/'
mkdir_p(fdir)
fname = fdir + 'econv.pdf'
fig.savefig(fname)
plt.show()
def energy_spectrum_multi(bg, spect_info, species='e'):
"""Plot energy spectra for runs with different guide field
"""
if species == 'h':
species = 'H'
bg_str = str(int(bg * 10)).zfill(2)
mimes = np.asarray([25, 100, 400])
tmaxs = np.asarray([114, 114, 102])
fig = plt.figure(figsize=[7, 5])
ax = fig.add_axes([0.15, 0.15, 0.8, 0.8])
COLORS = palettable.tableau.Tableau_10.mpl_colors
ax.set_prop_cycle('color', COLORS)
for mime, tmax in zip(mimes, tmaxs):
run_name = "mime" + str(mime) + "_beta002_bg" + bg_str
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
if species == 'e':
vth = pic_info.vthe
else:
vth = pic_info.vthi
gama = 1.0 / math.sqrt(1.0 - 3 * vth**2)
eth = gama - 1.0
fdir = '../data/spectra/' + run_name + '/'
fname = fdir + 'spectrum-' + species.lower() + '.' + str(tmax)
flog = np.fromfile(fname)
emin_log = math.log10(spect_info["emin"])
emax_log = math.log10(spect_info["emax"])
elog = 10**(np.linspace(emin_log, emax_log, spect_info["nbins"]))
flog *= np.gradient(elog)
elog /= eth
flog /= np.gradient(elog)
nptot = pic_info.nx * pic_info.ny * pic_info.nz * pic_info.nppc
flog /= nptot
ltext = r"$m_i/m_e=" + str(mime) + "$"
ax.loglog(elog, flog, linewidth=3, label=ltext)
ax.legend(loc=3,
prop={'size': 16},
ncol=1,
shadow=False,
fancybox=False,
frameon=False)
if species == 'e':
ax.set_xlim([3E-1, 5E2])
else:
ax.set_xlim([3E-1, 1E3])
ax.set_ylim([1E-11, 1E0])
ax.set_yticks(np.logspace(-10, 0, num=6))
ax.set_xlabel(r'$\varepsilon/\varepsilon_\text{th}$',
fontdict=FONT,
fontsize=20)
ax.set_ylabel(r'$f(\varepsilon)$', fontdict=FONT, fontsize=20)
ax.tick_params(labelsize=16)
fpath = "../img/img_high_mime/spectra/"
mkdir_p(fpath)
fname = fpath + "spect_bg" + bg_str + "_" + species + ".pdf"
fig.savefig(fname)
plt.show()
def plot_spectrum(plot_config):
"""Plot spectrum for all time frames for a single run
Args:
plot_config: plotting configuration
"""
species = plot_config["species"]
tstart = plot_config["tstart"]
tend = plot_config["tend"]
pic_run = plot_config["pic_run"]
root_dir = "/net/scratch3/xiaocanli/reconnection/Cori_runs/"
pic_run_dir = root_dir + pic_run + "/"
species = plot_config["species"]
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
if species == 'e':
vth = pic_info.vthe
sname = 'e'
else:
vth = pic_info.vthi
sname = 'i'
ebins = np.logspace(-6, 4, 1000)
gama = 1.0 / math.sqrt(1.0 - 3 * vth**2)
eth = gama - 1.0
emin, emax = 1E-6, 1E4
nband = 1000
dloge = (math.log10(emax) - math.log10(emin)) / (nband - 1)
emin0 = 10**(math.log10(emin) - dloge)
ebins = np.logspace(math.log10(emin0), math.log10(emax), nband + 1)
pbins = np.sqrt((ebins + 1)**2 - 1)
pbins /= np.sqrt((eth + 1)**2 - 1)
pbins_mid = (pbins[:-1] + pbins[1:]) * 0.5
dpbins = np.diff(pbins)
ebins /= eth
ebins_mid = (ebins[:-1] + ebins[1:]) * 0.5
debins = np.diff(ebins)
dt_particles = pic_info.dt_particles # in 1/wci
nframes = tend - tstart + 1
dtf = pic_info.fields_interval * pic_info.dtwci
fig = plt.figure(figsize=[7, 5])
rect = [0.13, 0.16, 0.7, 0.8]
ax = fig.add_axes(rect)
for tframe in range(tstart, tend + 1):
print("Time frame: %d" % tframe)
tindex = pic_info.fields_interval * tframe
fname = (pic_run_dir + "spectrum_combined/spectrum_" + species + "_" +
str(tindex) + ".dat")
spect = np.fromfile(fname, dtype=np.float32)
ndata, = spect.shape
spect[3:] /= np.diff(ebins)
spect[spect == 0] = np.nan
ax.loglog(ebins_mid,
spect[3:],
linewidth=1,
color=plt.cm.Spectral_r((tframe - tstart) / float(nframes),
1))
if species == 'e':
pindex = -5.5
power_index = "{%0.1f}" % pindex
pname = r'$\sim \varepsilon^{' + power_index + '}$'
fpower = 1E18 * ebins_mid**pindex
ax.loglog(ebins_mid, fpower, linewidth=1, color='k', linestyle='--')
else:
pindex = -3.5
power_index = "{%0.1f}" % pindex
pname = r'$\sim \varepsilon^{' + power_index + '}$'
fpower = 1E15 * ebins_mid**pindex
ax.loglog(ebins_mid, fpower, linewidth=1, color='k', linestyle='--')
ax.text(0.94,
0.85,
pname,
color='k',
fontsize=20,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax.tick_params(bottom=True, top=True, left=True, right=True)
ax.tick_params(axis='x', which='minor', direction='in', top=True)
ax.tick_params(axis='x', which='major', direction='in')
ax.tick_params(axis='y', which='minor', direction='in', left=True)
ax.tick_params(axis='y', which='major', direction='in')
if species in ['e', 'electron']:
ax.set_xlim([1E-1, 1E3])
else:
ax.set_xlim([1E-1, 5E3])
if '3D' in pic_run:
ax.set_ylim([1E0, 1E12])
ax.set_yticks(np.logspace(0, 10, num=6))
else:
ax.set_ylim([1E-1, 1E12])
ax.set_yticks(np.logspace(-1, 9, num=6))
ax.set_xlabel(r'$\varepsilon/\varepsilon_\text{th}$', fontsize=20)
ax.set_ylabel(r'$f(\varepsilon)$', fontsize=20)
ax.tick_params(labelsize=16)
rect_cbar = np.copy(rect)
rect_cbar[0] += rect[2] + 0.02
rect_cbar[2] = 0.03
cax = fig.add_axes(rect_cbar)
colormap = plt.cm.get_cmap('jet', tend - tstart + 1)
sm = plt.cm.ScalarMappable(cmap=plt.cm.Spectral_r,
norm=plt.Normalize(vmin=tstart * dtf,
vmax=tend * dtf))
cax.tick_params(axis='x', which='major', direction='in')
# fake up the array of the scalar mappable. Urgh...
sm._A = []
cbar = fig.colorbar(sm, cax=cax)
cbar.set_label(r'$t\Omega_{ci}$', fontsize=16)
cbar.ax.tick_params(labelsize=12)
fdir = '../img/cori_3d/spectrum/' + pic_run + '/'
mkdir_p(fdir)
fname = fdir + 'spectrum_' + species + '_2.pdf'
fig.savefig(fname)
plt.show()
def contour_tratio_beta(plot_config, show_plot=True):
"""
Plot contour of temperature ratio and parallel plasma beta
"""
pic_run = plot_config["pic_run"]
pic_run_dir = plot_config["pic_run_dir"]
species = plot_config["species"]
tframe = plot_config["tframe"]
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
fdir = '../data/temperature_anisotropy/' + pic_run + '/'
fname = fdir + 'ftratio_bpara_' + species + '_' + str(tframe) + '.gda'
fdata = np.fromfile(fname, dtype=np.float32)
nbins_tratio = int(fdata[0])
nbins_beta = int(fdata[1])
tratio_bins_edge = fdata[2:nbins_tratio + 3]
beta_bins_edge = fdata[nbins_tratio + 3:nbins_beta + nbins_tratio + 4]
tratio_bins_mid = 0.5 * (tratio_bins_edge[1:] + tratio_bins_edge[:-1])
dtratio = np.diff(tratio_bins_edge)
beta_bins_mid = 0.5 * (beta_bins_edge[1:] + beta_bins_edge[:-1])
dbeta = np.diff(beta_bins_edge)
ftratio_beta = fdata[nbins_beta + nbins_tratio + 4:]
ftratio_beta = ftratio_beta.reshape([nbins_beta, nbins_tratio]).T
delta = np.dot(dtratio[:, None], dbeta[None, :])
ftratio_beta /= delta
# ftratio_beta[ftratio_beta <= 0] = np.nan
tratio_min = tratio_bins_mid[0]
tratio_max = tratio_bins_mid[-1]
beta_min = beta_bins_mid[0]
beta_max = beta_bins_mid[-1]
# According to Bale et al. 2009,
# T_\perp / T_\parallel = 1 + a/(\beta_\parallel - \beta_0)^b.
# For mirror instability threshold, (a, b, \beta_0) = (0.77, 0.76, -0.016)
# For oblique firehose instability threshold, (a, b, \beta_0) = (-1.4, 1.0, -0.11)
tratio_firhose = 1 - 1.4 / (beta_bins_mid + 0.11)
tratio_mirror = 1 + 0.77 / (beta_bins_mid + 0.016)**0.76
tratio_firhose[tratio_firhose < 0] = np.nan
fig = plt.figure(figsize=[3.25, 2.5])
rect = [0.18, 0.16, 0.67, 0.8]
ax = fig.add_axes(rect)
ax.tick_params(bottom=True, top=True, left=True, right=True)
ax.tick_params(axis='x',
which='minor',
direction='in',
bottom=True,
top=True)
ax.tick_params(axis='x',
which='major',
direction='in',
bottom=True,
top=True)
ax.tick_params(axis='y', which='minor', direction='in')
ax.tick_params(axis='y', which='major', direction='in')
im = ax.pcolormesh(beta_bins_mid,
tratio_bins_mid,
ftratio_beta,
cmap=plt.cm.jet,
norm=LogNorm(vmin=1E0, vmax=1E12))
ax.set_xscale('log')
ax.set_yscale('log')
ax.plot(beta_bins_mid, tratio_firhose, color='k', linestyle='--')
ax.plot(beta_bins_mid, tratio_mirror, color='k', linestyle='--')
ax.set_xlim([1E-3, 1E3])
ax.set_ylim([1E-2, 1E2])
ax.tick_params(labelsize=8)
ax.set_xlabel(r'$\beta_\parallel$', fontsize=10)
ax.set_ylabel(r'$T_\perp/T_\parallel$', fontsize=10)
rect_cbar = np.copy(rect)
rect_cbar[0] += rect[2] + 0.02
rect_cbar[2] = 0.02
cbar_ax = fig.add_axes(rect_cbar)
cbar = fig.colorbar(im, cax=cbar_ax)
cbar.ax.tick_params(labelsize=8)
fdir = '../img/brazil_plot/' + pic_run + '/'
mkdir_p(fdir)
fname = fdir + 'tratio_bpara_' + species + '_' + str(tframe) + '.jpg'
fig.savefig(fname, dpi=300)
if show_plot:
plt.show()
else:
plt.close()
def save_mhd_config(run_name):
"""Save MHD configuration
Need to switch y and z directions
Args:
run_name: simulation run name
"""
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
double_data = np.zeros(13)
int_data = np.zeros(14, dtype=np.int32)
smime = math.sqrt(pic_info.mime)
lx = pic_info.lx_di * smime
ly = pic_info.lz_di * smime
lz = 0.0
nx = pic_info.nx
ny = pic_info.nz
nz = pic_info.ny
if nx > 1:
double_data[0] = lx / nx
else:
double_data[0] = 0.0
if ny > 1:
double_data[1] = ly / ny
else:
double_data[1] = 0.0
if nz > 1:
double_data[2] = lz / nz
else:
double_data[2] = 0.0
double_data[3] = 0.0
double_data[4] = -0.5 * ly
double_data[5] = 0.0
double_data[6] = lx
double_data[7] = 0.5 * ly
double_data[8] = 0.0
double_data[9] = lx
double_data[10] = ly
double_data[11] = lz
double_data[12] = pic_info.dtwpe * pic_info.fields_interval
int_data[0] = pic_info.nx
int_data[1] = pic_info.nz
int_data[2] = pic_info.ny
int_data[3] = pic_info.nx / pic_info.topology_x
int_data[4] = pic_info.nz / pic_info.topology_z
int_data[5] = pic_info.ny / pic_info.topology_y
int_data[6] = pic_info.topology_x
int_data[7] = pic_info.topology_z
int_data[8] = pic_info.topology_y
int_data[9] = 9
int_data[10] = 0 # Periodic boundary condition as default
int_data[11] = 0
int_data[12] = 0
int_data[13] = 0
fpath = run_dir + 'bin_data/'
mkdir_p(fpath)
fname = fpath + 'mhd_config.dat'
double_data.tofile(fname)
f = open(fname, 'a')
int_data.tofile(f)
f.close()
def transfer_to_h5part(plot_config):
"""Transfer current HDF5 file to H5Part format
All particles at the same time step are stored in the same time step
"""
pic_run = plot_config["pic_run"]
pic_run_dir = plot_config["pic_run_dir"]
traj_dir = plot_config["traj_dir"]
if plot_config["species"] == 'e':
species = 'electron'
else:
species = 'H'
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
fpath_traj = pic_run_dir + traj_dir + '/'
fname = fpath_traj + species + 's.h5p'
fh_in = h5py.File(fname, 'r')
particle_tags = list(fh_in.keys())
nptl = len(particle_tags)
ptl, ntf = read_particle_data(0, particle_tags, pic_info, fh_in)
Ux = np.zeros(ntf * nptl, dtype=ptl['Ux'].dtype)
Uy = np.zeros(ntf * nptl, dtype=ptl['Uy'].dtype)
Uz = np.zeros(ntf * nptl, dtype=ptl['Uz'].dtype)
dX = np.zeros(ntf * nptl, dtype=ptl['dX'].dtype)
dY = np.zeros(ntf * nptl, dtype=ptl['dY'].dtype)
dZ = np.zeros(ntf * nptl, dtype=ptl['dZ'].dtype)
i = np.zeros(ntf * nptl, dtype=ptl['i'].dtype)
q = np.zeros(ntf * nptl, dtype=ptl['q'].dtype)
gamma = np.zeros(ntf * nptl, dtype=ptl['gamma'].dtype)
t = np.zeros(ntf * nptl, dtype=ptl['t'].dtype)
if 'Bx' in ptl:
Bx = np.zeros(ntf * nptl, dtype=ptl['Bx'].dtype)
By = np.zeros(ntf * nptl, dtype=ptl['By'].dtype)
Bz = np.zeros(ntf * nptl, dtype=ptl['Bz'].dtype)
Ex = np.zeros(ntf * nptl, dtype=ptl['Ex'].dtype)
Ey = np.zeros(ntf * nptl, dtype=ptl['Ey'].dtype)
Ez = np.zeros(ntf * nptl, dtype=ptl['Ez'].dtype)
if 'Vx' in ptl:
Vx = np.zeros(ntf * nptl, dtype=ptl['Vx'].dtype)
Vy = np.zeros(ntf * nptl, dtype=ptl['Vy'].dtype)
Vz = np.zeros(ntf * nptl, dtype=ptl['Vz'].dtype)
ne = np.zeros(ntf * nptl, dtype=ptl['ne'].dtype)
ni = np.zeros(ntf * nptl, dtype=ptl['ni'].dtype)
# Read all particle data
for iptl in range(nptl):
ptl, ntf = read_particle_data(iptl, particle_tags, pic_info, fh_in)
Ux[iptl::nptl] = ptl['Ux']
Uy[iptl::nptl] = ptl['Uy']
Uz[iptl::nptl] = ptl['Uz']
dX[iptl::nptl] = ptl['dX']
dY[iptl::nptl] = ptl['dY']
dZ[iptl::nptl] = ptl['dZ']
i[iptl::nptl] = ptl['i']
q[iptl::nptl] = ptl['q']
gamma[iptl::nptl] = ptl['gamma']
t[iptl::nptl] = ptl['t']
if 'Bx' in ptl:
Bx[iptl::nptl] = ptl['Bx']
By[iptl::nptl] = ptl['By']
Bz[iptl::nptl] = ptl['Bz']
Ex[iptl::nptl] = ptl['Ex']
Ey[iptl::nptl] = ptl['Ey']
Ez[iptl::nptl] = ptl['Ez']
if 'Vx' in ptl:
Vx[iptl::nptl] = ptl['Vx']
Vy[iptl::nptl] = ptl['Vy']
Vz[iptl::nptl] = ptl['Vz']
ne[iptl::nptl] = ptl['ne']
ni[iptl::nptl] = ptl['ni']
fh_in.close()
fname_out = fpath_traj + species + 's.h5part'
fh_out = h5py.File(fname_out, 'w')
for tindex in range(0, ntf):
grp = fh_out.create_group('Step#' + str(tindex))
es, ee = tindex * nptl, (tindex + 1) * nptl
grp.create_dataset('Ux', (nptl, ), data=Ux[es:ee])
grp.create_dataset('Uy', (nptl, ), data=Uy[es:ee])
grp.create_dataset('Uz', (nptl, ), data=Uz[es:ee])
grp.create_dataset('dX', (nptl, ), data=dX[es:ee])
grp.create_dataset('dY', (nptl, ), data=dY[es:ee])
grp.create_dataset('dZ', (nptl, ), data=dZ[es:ee])
grp.create_dataset('i', (nptl, ), data=i[es:ee])
grp.create_dataset('q', (nptl, ), data=q[es:ee])
grp.create_dataset('gamma', (nptl, ), data=gamma[es:ee])
grp.create_dataset('t', (nptl, ), data=t[es:ee])
if 'Bx' in ptl:
grp.create_dataset('Bx', (nptl, ), data=Bx[es:ee])
grp.create_dataset('By', (nptl, ), data=By[es:ee])
grp.create_dataset('Bz', (nptl, ), data=Bz[es:ee])
grp.create_dataset('Ex', (nptl, ), data=Ex[es:ee])
grp.create_dataset('Ey', (nptl, ), data=Ey[es:ee])
grp.create_dataset('Ez', (nptl, ), data=Ez[es:ee])
if 'Vx' in ptl:
grp.create_dataset('Vx', (nptl, ), data=Vx[es:ee])
grp.create_dataset('Vy', (nptl, ), data=Vy[es:ee])
grp.create_dataset('Vz', (nptl, ), data=Vz[es:ee])
grp.create_dataset('ne', (nptl, ), data=ne[es:ee])
grp.create_dataset('ni', (nptl, ), data=ni[es:ee])
fh_out.close()
def plot_spectrum_both(plot_config, show_plot=True):
"""Plot spectrum both species
Args:
plot_config: plotting configuration
"""
species = plot_config["species"]
tstart = plot_config["tstart"]
tend = plot_config["tend"]
tframe = plot_config["tframe"]
pic_run = plot_config["pic_run"]
pic_run_dir = plot_config["pic_run_dir"]
species = plot_config["species"]
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
vthe = pic_info.vthe
gama = 1.0 / math.sqrt(1.0 - 3 * vthe**2)
ethe = gama - 1.0
vthi = pic_info.vthi
gama = 1.0 / math.sqrt(1.0 - 3 * vthi**2)
ethi = gama - 1.0
ebins = np.logspace(-6, 4, 1000)
ebins_e = ebins / ethe
ebins_i = ebins / ethi
dt_particles = pic_info.dt_particles # in 1/wci
nframes = tend - tstart + 1
dtf = math.ceil(pic_info.dt_particles / 0.1) * 0.1
fig = plt.figure(figsize=[3.5, 2.5])
rect = [0.16, 0.16, 0.8, 0.8]
ax = fig.add_axes(rect)
ax.set_prop_cycle('color', COLORS)
# for tframe in range(tstart_plot, tend_plot + 1):
print("Time frame: %d" % tframe)
tindex = pic_info.particle_interval * tframe
fname = (pic_run_dir + "spectrum_combined/spectrum_e_" + str(tindex) +
".dat")
spect_e = np.fromfile(fname, dtype=np.float32)
fname = (pic_run_dir + "spectrum_combined/spectrum_i_" + str(tindex) +
".dat")
spect_i = np.fromfile(fname, dtype=np.float32)
spect_e[3:] /= np.gradient(ebins_e)
spect_i[3:] /= np.gradient(ebins_i)
# initial thermal distribution
fname = (pic_run_dir + "spectrum_combined/spectrum_e_0.dat")
spect_init = np.fromfile(fname, dtype=np.float32)
ndata, = spect_init.shape
spect_init[3:] /= np.gradient(ebins_e)
ax.loglog(ebins_e,
spect_init[3:],
linewidth=1,
color='k',
linestyle='--',
label='initial')
ax.loglog(ebins_e, spect_e[3:], linewidth=1, label='electron')
ax.loglog(ebins_i, spect_i[3:], linewidth=1, label='ion')
ax.legend(loc=3,
prop={'size': 10},
ncol=1,
shadow=False,
fancybox=False,
frameon=False)
ax.tick_params(bottom=True, top=True, left=True, right=True)
ax.tick_params(axis='x', which='minor', direction='in', top='on')
ax.tick_params(axis='x', which='major', direction='in')
ax.tick_params(axis='y', which='minor', direction='in', left='on')
ax.tick_params(axis='y', which='major', direction='in')
ax.set_xlim([1E-1, 2E3])
ax.set_ylim([1E-1, 2E12])
ax.set_yticks(np.logspace(-1, 11, num=7))
ax.set_xlabel(r'$\varepsilon/\varepsilon_\text{th}$', fontsize=10)
ax.set_ylabel(r'$f(\varepsilon)$', fontsize=10)
ax.tick_params(labelsize=8)
fdir = '../img/cori_3d/spectrum/' + pic_run + '/both_species/'
mkdir_p(fdir)
fname = fdir + 'spectrum_both_' + str(tframe) + '.pdf'
fig.savefig(fname)
if show_plot:
plt.show()
else:
plt.close()
def test_fluid_energization():
"""Test fluid-based energization
"""
run_name = "mime25_beta002_guide00_frequent_dump"
species = "e"
jdote_name = '../data/jdote_data/jdote_' + run_name + '_' + species + '.json'
jdote = read_data_from_json(jdote_name)
jcurv_dote = jdote.jcpara_dote
jgrad_dote = jdote.jgrad_dote
jmag_dote = jdote.jmag_dote
jpara_dote = jdote.jqnupara_dote
jperp_dote = jdote.jqnuperp_dote
jdote_name = '../data/jpolar_dote/jpolar_dote_' + run_name + '_' + species + '.dat'
jpolar_dote = np.fromfile(jdote_name, np.float32)
fname = "../data/fluid_energization/"
fname += "emf_ptensor_" + species + '.gda'
fluid_ene = np.fromfile(fname, dtype=np.float32)
nvar = int(fluid_ene[0])
nframes = int(fluid_ene[1])
curv_drift_dote = fluid_ene[2:nframes+2]
grad_drift_dote = fluid_ene[nframes+2:2*nframes+2]
magnetization_dote = fluid_ene[2*nframes+2:3*nframes+2]
comp_ene = fluid_ene[3*nframes+2:4*nframes+2]
shear_ene = fluid_ene[4*nframes+2:5*nframes+2]
ptensor_ene = fluid_ene[5*nframes+2:6*nframes+2]
fname = "../data/fluid_energization/"
fname += "para_perp_acc_" + species + '.gda'
fluid_ene = np.fromfile(fname, dtype=np.float32)
nvar = int(fluid_ene[0])
nframes = int(fluid_ene[1])
acc_drift_dote = fluid_ene[2:nframes+2]
para_ene = fluid_ene[2*nframes+2:3*nframes+2]
perp_ene = fluid_ene[3*nframes+2:4*nframes+2]
acc_drift_dote = fluid_ene[2:nframes+2]
acc_drift_dote[-1] = acc_drift_dote[-2]
fname = "../data/fluid_energization/"
fname += "para_perp_acc_" + species + '.gda'
fluid_ene = np.fromfile(fname, dtype=np.float32)
nvar = int(fluid_ene[0])
nframes = int(fluid_ene[1])
acc_drift_dote = fluid_ene[2:nframes+2]
para_ene = fluid_ene[nframes+2:2*nframes+2]
perp_ene = fluid_ene[2*nframes+2:3*nframes+2]
acc_drift_dote[-1] = acc_drift_dote[-2]
# plt.plot(jcurv_dote, linewidth=2)
# plt.plot(jgrad_dote, linewidth=2)
# plt.plot(jmag_dote, linewidth=2)
# plt.plot(curv_drift_dote)
# plt.plot(grad_drift_dote)
# plt.plot(-magnetization_dote)
jperp_dote_n = curv_drift_dote + grad_drift_dote + magnetization_dote
jperp_dote_n += acc_drift_dote
plt.plot(jpara_dote + jperp_dote, linewidth=2)
# plt.plot(jperp_dote, linewidth=2)
# plt.plot(jpara_dote, linewidth=2)
# plt.plot(jdote.jpolar_dote, linewidth=2)
# plt.plot(jperp_dote_n)
plt.plot(para_ene + perp_ene)
# plt.plot(para_ene)
# plt.plot(perp_ene)
# plt.plot(ptensor_ene + acc_drift_dote)
# plt.plot(comp_ene + shear_ene)
# plt.plot(acc_drift_dote)
plt.show()
def plot_spectrum(plot_config):
"""Plot spectrum for all time frames for a single run
Args:
plot_config: plotting configuration
"""
species = plot_config["species"]
tstart = plot_config["tstart"]
tend = plot_config["tend"]
pic_run = plot_config["pic_run"]
root_dir = "/net/scratch3/xiaocanli/reconnection/Cori_runs/"
pic_run_dir = root_dir + pic_run + "/"
species = plot_config["species"]
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
if species == 'e':
vth = pic_info.vthe
sname = 'e'
else:
vth = pic_info.vthi
sname = 'i'
ebins = np.logspace(-4, 6, 1000)
if species in ['i', 'ion', 'proton']:
ebins *= pic_info.mime
dt_particles = pic_info.dt_particles # in 1/wci
nframes = tend - tstart + 1
dtf = math.ceil(pic_info.dt_particles / 0.1) * 0.1
fig = plt.figure(figsize=[7, 5])
rect = [0.13, 0.16, 0.7, 0.8]
ax = fig.add_axes(rect)
for tframe in range(tstart, tend + 1):
print("Time frame: %d" % tframe)
tindex = pic_info.eparticle_interval * tframe
fname = (pic_run_dir + "spectrum_combined/spectrum_" + species + "_" +
str(tindex) + ".dat")
spect = np.fromfile(fname, dtype=np.float32)
ndata, = spect.shape
spect[3:] /= np.gradient(ebins)
spect[spect == 0] = np.nan
ax.loglog(ebins,
spect[3:],
linewidth=1,
color=plt.cm.Spectral_r((tframe - tstart) / float(nframes),
1))
if species == 'e':
pindex = -2.5
power_index = "{%0.1f}" % pindex
pname = r'$\propto (\gamma - 1)^{' + power_index + '}$'
fpower = 1E13 * ebins**pindex
ax.loglog(ebins, fpower, linewidth=1, color='k', linestyle='--')
else:
pindex = -3.5
power_index = "{%0.1f}" % pindex
pname = r'$\propto (\gamma - 1)^{' + power_index + '}$'
fpower = 1E15 * ebins**pindex
ax.loglog(ebins, fpower, linewidth=1, color='k', linestyle='--')
ax.text(0.94,
0.85,
pname,
color='k',
fontsize=20,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax.tick_params(bottom=True, top=True, left=True, right=True)
ax.tick_params(axis='x', which='minor', direction='in', top=True)
ax.tick_params(axis='x', which='major', direction='in')
ax.tick_params(axis='y', which='minor', direction='in', left=True)
ax.tick_params(axis='y', which='major', direction='in')
if species in ['e', 'electron']:
ax.set_xlim([1E-1, 1E3])
else:
ax.set_xlim([1E-1, 1E3])
if '3D' in pic_run:
ax.set_ylim([1E0, 1E12])
ax.set_yticks(np.logspace(0, 10, num=6))
else:
ax.set_ylim([1E-1, 1E9])
ax.set_yticks(np.logspace(-1, 9, num=6))
text1 = r'$(\gamma - 1)m_' + species + r'c^2$'
ax.set_xlabel(text1, fontsize=20)
ax.set_ylabel(r'$f(\gamma - 1)$', fontsize=20)
ax.tick_params(labelsize=16)
rect_cbar = np.copy(rect)
rect_cbar[0] += rect[2] + 0.02
rect_cbar[2] = 0.03
cax = fig.add_axes(rect_cbar)
colormap = plt.cm.get_cmap('jet', tend - tstart + 1)
sm = plt.cm.ScalarMappable(cmap=plt.cm.Spectral_r,
norm=plt.Normalize(vmin=tstart * dtf,
vmax=tend * dtf))
cax.tick_params(axis='x', which='major', direction='in')
# fake up the array of the scalar mappable. Urgh...
sm._A = []
cbar = fig.colorbar(sm, cax=cax)
cbar.set_label(r'$t\Omega_{ci}$', fontsize=16)
cbar.ax.tick_params(labelsize=12)
fdir = '../img/cori_3d/spectrum/' + pic_run + '/'
mkdir_p(fdir)
fname = fdir + 'spectrum_' + species + '_2.pdf'
fig.savefig(fname)
plt.show()
def magnetic_power_spectrum(plot_config, show_plot=True):
"""Plot power spectrum of magnetic field
Args:
plot_config: plot configuration
"""
tframe = plot_config["tframe"]
bg = plot_config["bg"]
pic_runs = ["2D-Lx150-bg" + str(bg) + "-150ppc-16KNL"]
pic_runs.append("3D-Lx150-bg" + str(bg) + "-150ppc-2048KNL")
pic_run = pic_runs[1]
root_dir = "/net/scratch3/xiaocanli/reconnection/Cori_runs/"
pic_run_dir = root_dir + pic_run + "/"
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
k1, k2 = 0.03, 1.0
nkbins = 100
pindex = -2.7
kpower = np.logspace(math.log10(k1), math.log10(k2), 100)
fpower3 = kpower**pindex / 1E4
fig = plt.figure(figsize=[3.5, 2.5])
rect = [0.17, 0.16, 0.78, 0.8]
ax = fig.add_axes(rect)
COLORS = palettable.tableau.Tableau_10.mpl_colors
ax.set_prop_cycle('color', COLORS)
tframes = range(10, 36, 5)
for tframe in tframes:
tindex = tframe * pic_info.fields_interval
for ivar, var in enumerate(["bx", "by", "bz"]):
fdir = ('../data/power_spectrum/' + pic_run + '/power_spectrum_' +
var + '/')
fname = fdir + var + str(tindex) + '.para'
kpara, fdata = read_power_spectrum(fname)
if ivar > 0:
fkpara += fdata
else:
fkpara = fdata
fname = fdir + var + str(tindex) + '.perp'
kperp, fdata = read_power_spectrum(fname)
if ivar > 0:
fkperp += fdata
else:
fkperp = fdata
ax.loglog(kperp, fkperp, linewidth=1, label=r'$k_\perp$')
label1 = r'$\propto k_\perp^{' + str(pindex) + '}$'
ax.loglog(kpower,
fpower3,
linewidth=0.5,
color='k',
linestyle='--',
label=label1)
ax.tick_params(bottom=True, top=True, left=True, right=True)
ax.tick_params(axis='x', which='minor', direction='in', top=False)
ax.tick_params(axis='x', which='major', direction='in')
ax.tick_params(axis='y', which='minor', direction='in', left=False)
ax.tick_params(axis='y', which='major', direction='in')
twci = math.ceil((tframe * pic_info.dt_fields) / 0.1) * 0.1
# text1 = r'$t\Omega_{ci}=' + ("{%0.0f}" % twci) + '$'
ax.text(0.7,
0.47,
label1,
color='k',
fontsize=10,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
ax.set_xlim([1E-2, 5E0])
# ax.set_ylim([1E-7, 2E-1])
ax.set_yticks((np.logspace(-7, -1, 4)))
ax.set_xlabel(r'$k_\perp d_e$', fontsize=10)
ax.set_ylabel(r'$E_B(k_\perp)$', fontsize=10)
ax.tick_params(labelsize=8)
# Embedded plot for energy evolution
rect1 = [0.29, 0.29, 0.30, 0.28]
ax1 = fig.add_axes(rect1)
ax1.tick_params(bottom=True, top=True, left=True, right=True)
ax1.tick_params(axis='x', which='minor', direction='in', top=False)
ax1.tick_params(axis='x', which='major', direction='in')
ax1.tick_params(axis='y', which='minor', direction='in')
ax1.tick_params(axis='y', which='major', direction='in')
for irun, pic_run in enumerate(pic_runs):
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
enorm = pic_info.ene_magnetic[0]
ene_bx = pic_info.ene_bx
ene_by = pic_info.ene_by
ene_bz = pic_info.ene_bz
ene_magnetic = pic_info.ene_magnetic
ene_electric = pic_info.ene_electric
kene_e = pic_info.kene_e
kene_i = pic_info.kene_i
ene_bx /= enorm
ene_by /= enorm
ene_bz /= enorm
ene_magnetic /= enorm
kene_e /= enorm
kene_i /= enorm
tenergy = pic_info.tenergy
lstyle = '-' if '3D' in pic_run else '--'
ax1.plot(tenergy,
ene_magnetic,
linewidth=1,
linestyle=lstyle,
color='k')
ax1.set_ylim([0.66, 1.02])
# for iframe, tframe in enumerate(tframes):
# twci = tframe * pic_info.dt_fields
# ax1.plot([twci, twci], ax1.get_ylim(), linewidth=0.5,
# linestyle=':', color=COLORS[iframe])
tframes = np.asarray(tframes) * pic_info.dt_fields
nframe, = tframes.shape
ax1.scatter(tframes, [0.7] * nframe,
c=COLORS[:nframe],
marker='x',
s=10,
linewidth=0.5)
ax1.text(0.6,
0.28,
"2D",
color='k',
fontsize=6,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none',
pad=10.0),
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
ax1.text(0.6,
0.52,
"3D",
color='k',
fontsize=6,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none',
pad=10.0),
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
ax1.tick_params(labelsize=6)
ax1.set_xlabel(r'$t\Omega_{ci}$', fontsize=6)
ax1.set_ylabel(r'$\varepsilon_B/\varepsilon_{B0}$', fontsize=6)
ax1.set_xlim([0, 400])
fdir = '../img/cori_3d/power_spectrum_pub/' + pic_run + '/'
mkdir_p(fdir)
fname = fdir + 'mag_perp.pdf'
fig.savefig(fname)
if show_plot:
plt.show()
else:
plt.close('all')
def transfer_to_h5part(plot_config):
"""Transfer current HDF5 file to H5Part format
All particles at the same time step are stored in the same time step
"""
pic_run = plot_config["pic_run"]
pic_run_dir = plot_config["pic_run_dir"]
traj_dir = plot_config["traj_dir"]
if plot_config["species"] == 'e':
species = 'electron'
else:
species = 'H'
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
fpath_traj = pic_run_dir + traj_dir + '/'
fname = fpath_traj + species + 's.h5p'
fh_in = h5py.File(fname, 'r')
particle_tags = list(fh_in.keys())
nptl = len(particle_tags)
ptl, ntf = read_particle_data(0, particle_tags, pic_info, fh_in)
Ux = np.zeros(ntf * nptl, dtype=ptl['Ux'].dtype)
Uy = np.zeros(ntf * nptl, dtype=ptl['Uy'].dtype)
Uz = np.zeros(ntf * nptl, dtype=ptl['Uz'].dtype)
dX = np.zeros(ntf * nptl, dtype=ptl['dX'].dtype)
dY = np.zeros(ntf * nptl, dtype=ptl['dY'].dtype)
dZ = np.zeros(ntf * nptl, dtype=ptl['dZ'].dtype)
i = np.zeros(ntf * nptl, dtype=ptl['i'].dtype)
q = np.zeros(ntf * nptl, dtype=ptl['q'].dtype)
gamma = np.zeros(ntf * nptl, dtype=ptl['gamma'].dtype)
t = np.zeros(ntf * nptl, dtype=ptl['t'].dtype)
if 'Bx' in ptl:
Bx = np.zeros(ntf * nptl, dtype=ptl['Bx'].dtype)
By = np.zeros(ntf * nptl, dtype=ptl['By'].dtype)
Bz = np.zeros(ntf * nptl, dtype=ptl['Bz'].dtype)
Ex = np.zeros(ntf * nptl, dtype=ptl['Ex'].dtype)
Ey = np.zeros(ntf * nptl, dtype=ptl['Ey'].dtype)
Ez = np.zeros(ntf * nptl, dtype=ptl['Ez'].dtype)
if 'Vx' in ptl:
Vx = np.zeros(ntf * nptl, dtype=ptl['Vx'].dtype)
Vy = np.zeros(ntf * nptl, dtype=ptl['Vy'].dtype)
Vz = np.zeros(ntf * nptl, dtype=ptl['Vz'].dtype)
ne = np.zeros(ntf * nptl, dtype=ptl['ne'].dtype)
ni = np.zeros(ntf * nptl, dtype=ptl['ni'].dtype)
# Read all particle data
for iptl in range(nptl):
ptl, ntf = read_particle_data(iptl, particle_tags, pic_info, fh_in)
Ux[iptl::nptl] = ptl['Ux']
Uy[iptl::nptl] = ptl['Uy']
Uz[iptl::nptl] = ptl['Uz']
dX[iptl::nptl] = ptl['dX']
dY[iptl::nptl] = ptl['dY']
dZ[iptl::nptl] = ptl['dZ']
i[iptl::nptl] = ptl['i']
q[iptl::nptl] = ptl['q']
gamma[iptl::nptl] = ptl['gamma']
t[iptl::nptl] = ptl['t']
if 'Bx' in ptl:
Bx[iptl::nptl] = ptl['Bx']
By[iptl::nptl] = ptl['By']
Bz[iptl::nptl] = ptl['Bz']
Ex[iptl::nptl] = ptl['Ex']
Ey[iptl::nptl] = ptl['Ey']
Ez[iptl::nptl] = ptl['Ez']
if 'Vx' in ptl:
Vx[iptl::nptl] = ptl['Vx']
Vy[iptl::nptl] = ptl['Vy']
Vz[iptl::nptl] = ptl['Vz']
ne[iptl::nptl] = ptl['ne']
ni[iptl::nptl] = ptl['ni']
fh_in.close()
fname_out = fpath_traj + species + 's.h5part'
fh_out = h5py.File(fname_out, 'w')
for tindex in range(0, ntf):
grp = fh_out.create_group('Step#' + str(tindex))
es, ee = tindex * nptl, (tindex + 1) * nptl
grp.create_dataset('Ux', (nptl, ), data=Ux[es:ee])
grp.create_dataset('Uy', (nptl, ), data=Uy[es:ee])
grp.create_dataset('Uz', (nptl, ), data=Uz[es:ee])
grp.create_dataset('dX', (nptl, ), data=dX[es:ee])
grp.create_dataset('dY', (nptl, ), data=dY[es:ee])
grp.create_dataset('dZ', (nptl, ), data=dZ[es:ee])
grp.create_dataset('i', (nptl, ), data=i[es:ee])
grp.create_dataset('q', (nptl, ), data=q[es:ee])
grp.create_dataset('gamma', (nptl, ), data=gamma[es:ee])
grp.create_dataset('t', (nptl, ), data=t[es:ee])
if 'Bx' in ptl:
grp.create_dataset('Bx', (nptl, ), data=Bx[es:ee])
grp.create_dataset('By', (nptl, ), data=By[es:ee])
grp.create_dataset('Bz', (nptl, ), data=Bz[es:ee])
grp.create_dataset('Ex', (nptl, ), data=Ex[es:ee])
grp.create_dataset('Ey', (nptl, ), data=Ey[es:ee])
grp.create_dataset('Ez', (nptl, ), data=Ez[es:ee])
if 'Vx' in ptl:
grp.create_dataset('Vx', (nptl, ), data=Vx[es:ee])
grp.create_dataset('Vy', (nptl, ), data=Vy[es:ee])
grp.create_dataset('Vz', (nptl, ), data=Vz[es:ee])
grp.create_dataset('ne', (nptl, ), data=ne[es:ee])
grp.create_dataset('ni', (nptl, ), data=ni[es:ee])
fh_out.close()
def pspect_mag_vel(plot_config, show_plot=True):
"""Plot power spectrum for magnetic field and velocity field
Args:
plot_config: plot configuration
"""
pic_run = plot_config["pic_run"]
pic_run_dir = plot_config["pic_run_dir"]
tframe = plot_config["tframe"]
component = plot_config["component"]
picinfo_fname = '../data/pic_info/pic_info_' + pic_run + '.json'
pic_info = read_data_from_json(picinfo_fname)
tindex = tframe * pic_info.fields_interval
var_name = 'vi' + component
fdir = '../data/power_spectrum/' + pic_run + '/power_spectrum_' + var_name + '/'
fname = fdir + var_name + str(tindex) + '.para'
kpara, fkpara_v = read_power_spectrum(fname)
fname = fdir + var_name + str(tindex) + '.perp'
kperp, fkperp_v = read_power_spectrum(fname)
# fkpara_v *= pic_info.mime * 0.5
# fkperp_v *= pic_info.mime * 0.5
var_name = 'b' + component
fdir = '../data/power_spectrum/' + pic_run + '/power_spectrum_' + var_name + '/'
fname = fdir + var_name + str(tindex) + '.para'
kpara, fkpara_b = read_power_spectrum(fname)
fname = fdir + var_name + str(tindex) + '.perp'
kperp, fkperp_b = read_power_spectrum(fname)
k1, k2 = 0.05, 0.5
nkbins = 100
id_para = (np.abs(kpara - k1)).argmin()
id_perp = (np.abs(kperp - k1)).argmin()
fnorm1 = max(fkpara_b[id_para], fkperp_b[id_perp]) * 2
fnorm2 = max(fkpara_v[id_para], fkperp_v[id_perp]) * 4
kpower = np.logspace(math.log10(k1), math.log10(k2), 100)
fpower1 = kpower**(-5 / 3)
fpower1 /= fpower1[0]
fpower2 = kpower**-1.5
fpower2 *= fpower1[0] / fpower2[0]
fpower3 = kpower**-2
fpower3 *= fpower1[0] / fpower3[0]
fig = plt.figure(figsize=[7, 5])
rect = [0.15, 0.16, 0.8, 0.8]
ax = fig.add_axes(rect)
ax.set_prop_cycle('color', COLORS)
p1, = ax.loglog(kpara, fkpara_b, linewidth=2, label=r'$E_B(k_\parallel)$')
p2, = ax.loglog(kperp, fkperp_b, linewidth=2, label=r'$E_B(k_\perp)$')
p3, = ax.loglog(kpara,
fkpara_v,
linewidth=2,
linestyle='--',
color=p1.get_color(),
label=r'$E_V(k_\parallel)$')
p4, = ax.loglog(kperp,
fkperp_v,
linewidth=2,
linestyle='--',
color=p2.get_color(),
label=r'$E_V(k_\perp)$')
ax.loglog(kpower,
fpower1 * fnorm1,
linewidth=1,
color='k',
linestyle='--',
label=r'$\sim k^{-5/3}$')
ax.loglog(kpower,
fpower2 * fnorm1,
linewidth=1,
color='k',
linestyle='-.',
label=r'$\sim k^{-3/2}$')
ax.loglog(kpower,
fpower3 * fnorm1,
linewidth=1,
color='k',
linestyle=':',
label=r'$\sim k^{-2}$')
ax.loglog(kpower, fpower1 * fnorm2, linewidth=1, color='k', linestyle='--')
ax.loglog(kpower, fpower2 * fnorm2, linewidth=1, color='k', linestyle='-.')
ax.loglog(kpower, fpower3 * fnorm2, linewidth=1, color='k', linestyle=':')
ax.legend(loc=1,
prop={'size': 16},
ncol=1,
shadow=False,
fancybox=False,
frameon=False)
ax.tick_params(bottom=True, top=True, left=True, right=True)
ax.tick_params(axis='x', which='minor', direction='in', top='on')
ax.tick_params(axis='x', which='major', direction='in')
ax.tick_params(axis='y', which='minor', direction='in', left='on')
ax.tick_params(axis='y', which='major', direction='in')
ax.set_xlim([1E-2, 1E1])
ax.set_ylim([5E-9, 2E-1])
ax.set_xlabel(r'$kd_e$', fontsize=20)
ax.set_ylabel(r'$E(k)$', fontsize=20)
ax.tick_params(labelsize=16)
fdir = '../img/power_spectrum/' + pic_run + '/mag_vel/'
mkdir_p(fdir)
fname = fdir + 'bvel_' + component + '_para_perp_' + str(tframe) + '.pdf'
fig.savefig(fname)
if show_plot:
plt.show()
else:
plt.close('all')
def plot_energy_spectrum(run_name, spect_info, species='e', show_plot=True):
"""Plot particle energy spectrum
Args:
run_name: PIC simulation run name
species: 'e' for electrons, 'H' for ions
spect_info: dictionary for spectra information
"""
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
if species == 'h':
species = 'H'
# ntf = spect_info["tmax"]
ntf = pic_info.ntf
if species == 'e':
vth = pic_info.vthe
else:
vth = pic_info.vthi
gama = 1.0 / math.sqrt(1.0 - 3 * vth**2)
eth = gama - 1.0
emin_log = math.log10(spect_info["emin"])
emax_log = math.log10(spect_info["emax"])
nbins = spect_info["nbins"]
elog = 10**(np.linspace(emin_log, emax_log, nbins))
elog /= eth
nptot = pic_info.nx * pic_info.ny * pic_info.nz * pic_info.nppc
tframes = range(0, ntf - 1)
nframes = len(tframes)
flogs = np.zeros((nframes, nbins))
fig = plt.figure(figsize=[7, 5])
rect = [0.14, 0.16, 0.82, 0.8]
ax = fig.add_axes(rect)
for iframe, tframe in enumerate(tframes):
print("Time frame: %d" % tframe)
fdir = '../data/spectra/' + run_name + '/'
fname = fdir + 'spectrum-' + species.lower() + '.' + str(tframe)
flog = np.fromfile(fname)
flog /= nptot
color = plt.cm.jet(tframe / float(ntf), 1)
flogs[iframe, :] = flog
# ax.loglog(elog, flog, linewidth=2, color=color)
# fmin = np.min(flogs[np.nonzero(flogs)])
fmin = 1E-9
flogs += fmin
fdata = np.diff(np.log10(flogs[:, 400:600]), axis=0).T
ng = 3
kernel = np.ones((ng, ng)) / float(ng * ng)
fdata = signal.convolve2d(fdata, kernel, mode='same')
ax.imshow(fdata,
vmin=-1E-1,
vmax=1E-1,
cmap=plt.cm.seismic,
origin='lower',
interpolation='bicubic')
# if species == 'e':
# ax.set_xlim([1E0, 5E2])
# ax.set_ylim([1E-9, 1E2])
# else:
# ax.set_xlim([1E0, 2E3])
# ax.set_ylim([1E-7, 1E4])
ax.tick_params(bottom=True, top=True, left=True, right=False)
ax.tick_params(axis='x', which='minor', direction='in')
ax.tick_params(axis='x', which='major', direction='in')
ax.tick_params(axis='y', which='minor', direction='in')
ax.tick_params(axis='y', which='major', direction='in')
ax.set_xlabel(r'$\varepsilon/\varepsilon_\text{th}$',
fontdict=FONT,
fontsize=20)
ax.set_ylabel(r'$f(\varepsilon)$', fontdict=FONT, fontsize=20)
ax.tick_params(labelsize=16)
ename = 'electron' if species == 'e' else 'ion'
fpath = "../img/img_high_mime/spectra/" + ename + "/"
mkdir_p(fpath)
fname = fpath + "spect_time_" + run_name + "_" + species + ".pdf"
fig.savefig(fname)
if show_plot:
plt.show()
else:
plt.close()
def transfer_pic_to_mhd(run_dir, run_name, tframe):
"""Transfer the required fields
Args:
run_dir: simulation directory
run_name: name of the simulation run
tframe: time frame
boundary_x: boundary condition along x-direction
0 for periodic; 1 for others
boundary_z: boundary condition along z-direction
"""
print("Time frame: %d" % tframe)
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
lx_di = pic_info.lx_di
lz_di = pic_info.lz_di
kwargs = {
"current_time": tframe,
"xl": 0,
"xr": lx_di,
"zb": -0.5 * lz_di,
"zt": 0.5 * lz_di
}
fname = run_dir + "data/bx.gda"
x, z, bx = read_2d_fields(pic_info, fname, **kwargs)
fname = run_dir + "data/by.gda"
x, z, by = read_2d_fields(pic_info, fname, **kwargs)
fname = run_dir + "data/bz.gda"
x, z, bz = read_2d_fields(pic_info, fname, **kwargs)
nx, = x.shape
nz, = z.shape
mhd_data = np.zeros((nz + 4, nx + 4, 8), dtype=np.float32)
# We need to switch y and z directions
absB = np.sqrt(bx**2 + by**2 + bz**2)
mhd_data[2:nz + 2, 2:nx + 2, 4] = bx
mhd_data[2:nz + 2, 2:nx + 2, 5] = bz
mhd_data[2:nz + 2, 2:nx + 2, 6] = -by
mhd_data[2:nz + 2, 2:nx + 2, 7] = absB
del bx, by, bz, absB
mime = pic_info.mime
# Electron
fname = run_dir + "data/ne.gda"
x, z, ne = read_2d_fields(pic_info, fname, **kwargs)
fname = run_dir + "data/vex.gda"
x, z, vex = read_2d_fields(pic_info, fname, **kwargs)
fname = run_dir + "data/vey.gda"
x, z, vey = read_2d_fields(pic_info, fname, **kwargs)
fname = run_dir + "data/vez.gda"
x, z, vez = read_2d_fields(pic_info, fname, **kwargs)
vx = ne * vex
vy = ne * vey
vz = ne * vez
del vex, vey, vez
# Ion
fname = run_dir + "data/ni.gda"
x, z, ni = read_2d_fields(pic_info, fname, **kwargs)
fname = run_dir + "data/vix.gda"
x, z, vix = read_2d_fields(pic_info, fname, **kwargs)
fname = run_dir + "data/viy.gda"
x, z, viy = read_2d_fields(pic_info, fname, **kwargs)
fname = run_dir + "data/viz.gda"
x, z, viz = read_2d_fields(pic_info, fname, **kwargs)
imass = 1.0 / (ne + ni * mime)
vx = (vx + ni * mime * vix) * imass
vy = (vy + ni * mime * viy) * imass
vz = (vz + ni * mime * viz) * imass
del vix, viy, viz, ne, ni, imass
absV = np.sqrt(vx**2 + vy**2 + vz**2)
mhd_data[2:nz + 2, 2:nx + 2, 0] = vx
mhd_data[2:nz + 2, 2:nx + 2, 1] = vz
mhd_data[2:nz + 2, 2:nx + 2, 2] = -vy
mhd_data[2:nz + 2, 2:nx + 2, 3] = absV
del vx, vy, vz, absV
# Assuming periodic boundary along x for fields and particles
# Assuming conducting boundary along z for fields and reflective for particles
mhd_data[:, 0:2, :] = mhd_data[:, nx - 1:nx + 1, :]
mhd_data[:, nx + 2:, :] = mhd_data[:, 3:5, :]
mhd_data[0:2, :, :] = mhd_data[3:1:-1, :, :]
mhd_data[nz + 2:, :, :] = mhd_data[nz + 1:nz - 1:-1, :, :]
fpath = run_dir + 'bin_data/'
mkdir_p(fpath)
fname = fpath + 'mhd_data_' + str(tframe).zfill(4)
print(mhd_data.shape)
print(np.isfortran(mhd_data))
mhd_data.tofile(fname)
